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Towards a One Health approach to WASH to tackle zoonotic disease and promote health and wellbeing
Get PDFThere is increasing awareness that exposure to animal faeces contributes to the global burden of diarrheal disease, as well as other zoonotic diseases. This recognition has prompted a re-evaluation of water, sanitation, and hygiene (WASH) interventions to address animal-related transmission pathways. However, current efforts focus primarily on animal faeces within household environments, neglecting other critical human-animal interactions that favour contamination such as animal handling. We advance growing efforts to link One Health and WASH from a risk perspective, reviewing implications for humans, animals, as well as the environment, which has been overlooked. We then discuss how a comprehensive OH-WASH approach can move beyond risks to also enable opportunities to promote health, equity, climate resilience, and other benefits. This framing offers possibilities to reduce disease transmission and enhance biosecurity, while addressing interconnected challenges facing low- and middle-income countries including food insecurity and agricultural livelihoods, animal health and welfare, and ecosystem degradation from excessive nutrients found in excret
Pseudo effects:How method biases can produce spurious findings about close relationships
No full textResearch on interpersonal relationships frequently relies on accurate self-reporting across various relationship facets (e.g., conflict, trust, appreciation). Yet shared method biases—which may greatly inflate associations between measures—are rarely accounted for during measurement validation or hypothesis testing. To examine how method biases can affect relationship research, we embarked on the ironic exploration of a new construct—Pseudo—comprised of irrelevant relationship evaluations (e.g., “My relationship has very good Saturn”). Pseudo was moderately associated with common relationship measures (e.g., satisfaction, commitment) and predicted those measures 3 weeks later. Results of a dyadic longitudinal study suggested that Pseudo taps into method biases, particularly sentiment override (i.e., people’s tendency to project their global relationship sentiments onto every relationship evaluation). We conclude that psychometric standards must be sufficiently rigorous to distinguish genuine constructs and associations from methodological artifacts that can otherwise pose a serious validity threat.</p
The importance of small island populations for the long-term survival of endangered large-bodied insular mammals
Get PDFIsland populations of large vertebrates have experienced higher extinction rates than mainland populations over long timescales due to demographic stochasticity, genetic drift, and inbreeding. While being more susceptible to extinction and as such potentially targeted for conservation interventions such as genetic rescue, small-island populations can experience relatively less anthropogenic habitat degradation than those on larger islands. Here, we determine the consequences and conservation implications of long-term isolation and recent human activities on genetic diversity of island populations of two forest-dependent mammals endemic to the Wallacea archipelago: the anoa ( Bubalus spp.) and babirusa ( Babyrousa spp.). Using genomic analyses and habitat suitability models, we show that, compared to closely related species, populations on mainland Sulawesi exhibit low heterozygosity, high inbreeding, a high proportion of deleterious alleles, and experience a high rate of anthropogenic disturbance. In contrast, populations on smaller islands occupy higher-quality habitats, possess fewer deleterious mutations despite exhibiting lower heterozygosity and higher inbreeding. Site frequency spectra indicate that these patterns reflect stronger, long-term purging in smaller-island populations. Our results thus suggest that conservation efforts should focus on protecting small-island high-quality habitats and avoiding translocations from mainland populations. This study highlights the crucial role of small offshore islands for the long-term survival of Wallacea's iconic and indigenous mammals in the face of development on the mainland. </p
Trustworthy Prediction with Gaussian Process Knowledge Scores
Get PDFProbabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are well-informed by previously seen data. In this paper, we propose a knowledge score for predictions from Gaussian process regression (GPR) models that quantifies the extent to which observing data have reduced our uncertainty about a prediction. The knowledge score is interpretable and naturally bounded between 0 and 1. We demonstrate in several experiments that the knowledge score can anticipate when predictions from a GPR model are accurate, and that this anticipation improves performance in tasks such as anomaly detection, extrapolation, and missing data imputation. Source code for this project is available online at https://github.com/KurtButler/GP-knowledge
Drivers of antimicrobial resistance in pig production systems of Uganda
Get PDFIncreasing protein demand in low- and middle-income countries may accelerate livestock intensification, antibiotic overuse and antimicrobial resistance (AMR) risk. Here, we examined Uganda's growing pig sector, tracking 70 farmers and their pigs in semi-intensive and free-range systems for a year. We investigated AMR and AMR gene abundance of 668 Escherichia coli, Klebsiella and DNA isolated from 877 faecal samples using diffusion disc-method and qPCR, respectively. Pigs in semi-intensive systems were 2.2 times more likely to exhibit AMR and had higher ermB levels. AMR in free-range farmers was twice that of pigs but still 1.4 times less likely than in semi-intensive systems. AMR prevalence increased by 0.76% per month. Potential transmission events were more likely on semi-intensive farms (OR = 3.16, 95% CI: 2.1-4.3, P < 0.001), especially when farmers had higher tetQ levels than pigs; the reverse was true for ermB. Intensified urban pig production may elevate AMR risks, underscoring the need for targeted interventions. </p
Direct numerical simulation of soot break-through in turbulent non-premixed flames
Get PDFSoot break-through, or “leakage”, is the transport of soot particles into fuel-lean regions leading to smoke emissions even when the global equivalence ratio is below unity. Under turbulent conditions, this phenomenon is affected by small-scale mixing, and a better understanding of the non-linear interactions between soot and gas-phase is crucial to improving the prediction of the existing reduced-order model in case of soot break-through. To this end, three Direct Numerical Simulations (DNSs) of temporally evolving turbulent non-premixed jet flames have been performed to study the later stages of soot evolution in turbulent flames. Various degrees of soot break-through are obtained by enforcing three different levels of flame extinction by rescaling the geometry and flow conditions, resulting in a variation in Damköhler number, while the Reynolds number remains constant. The simulations employ a detailed chemical mechanism for the gas phase chemistry and a moment method for modeling the soot number density function evolution. Both soot evolution and turbulence-chemistry-soot interaction are discussed in mixture fraction space and along Lagrangian trajectories in physical space. The results reveal that soot break-through is strongly correlated with both high mixture fraction dissipation rates and strong soot transport in mixture fraction space. Due to the higher mixture fraction dissipation rate, the lowest Damköhler number case exhibits more significant extinction, allowing significant soot break-through. Additionally, soot particles in the lean regions are shown to be smaller than those inside the flame due to the smaller residence times in the fuel-rich growth regions, with a significant probability of leaked soot particles at incipient size. From the Lagrangian statistics, the ratio of the mixture fraction diffusion rate and soot oxidation rate constant is shown to be a suitable parameter to identify soot break-through events. Overall, soot break-through is found to be dominated by local flame extinctions and, while a fast drift of soot toward lean regions is required for such phenomena, an accurate prediction of local extinctions is also necessary to capture soot break-through occurrences. Novelty and Significance This study presents new insights into turbulent sooting flames through a novel dataset, obtained using direct numerical simulations (DNSs), to investigate the later stages of soot evolution, including incomplete oxidation phenomena leading to smoking behavior. In contrast to existing DNS datasets which have primarily focused only on the early stages of soot formation and growth, the dataset presented and analyzed in this study provides key new data on soot oxidation and break-through in turbulent non-premixed flames. As modeling turbulence-chemistry-soot interactions is still an open research question in the combustion community, this dataset contributes to advancing the current understanding of the underlying physics governing soot evolution in turbulent flames.</p
Twelve principles for transformation-focused evaluation
Get PDFThere are growing calls for societies to transform towards regenerative futures that support the flourishing of life and transcend interlinked social and ecological crises. Many actors are now trying to identify whether desired transformations are occurring, and how different interventions are contributing to transformation. These are questions of evaluation. However, we lack a holistic appreciation of how evaluation can most effectively assist transformation efforts. We undertake a systematized scoping review of a large body of evaluation literature to identify principles and methodologies for transformation-focused evaluation. We identify twelve distinct but interdependent principles which, when applied by evaluators (and supported by evaluation commissioners, sponsors and funders), are considered to significantly enhance the effectiveness of evaluation in assessing and assisting transformations, although tensions exist between some of the principles. The principles were clustered into three overarching themes of Complexity Principles (how evaluation approaches complex systems), Power Principles (evaluators’ power relations), and Purpose Principles (evaluation’s deeper purpose and values). The Complexity Principles call for transformation-focused evaluators to appreciate evaluands (e.g., a transition initiative, sector, or region) as unique, entangled, nested, dynamic and uncertain systems, and employ more diverse, developmental, contextually adapted and future-sensitive evaluation approaches. The Power Principles urge evaluators to promote justice, embrace diverse and marginalized perspectives, and reduce evaluator-evaluand polarization by shifting power towards evaluation users, whilst also fostering a more autonomous evaluation profession and mutualistic partnerships of knowledge and action. Most fundamentally, the Purpose Principles advocate for a recentering of values, reflexivity and learning at the heart of evaluation practice, and ensure that the core purpose of evaluation is to support systemic, societal transformation towards regenerative futures. Relevant methodologies are suggested for operationalizing the principles. Transformation-focused evaluation is a radical shift from conventional practice but the urgency to address global crises makes the shift a crucial one
A stochastic approach to Bi-Level optimization for hyperparameter optimization and meta learning
Get PDFWe tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learning, invariance learning and more. These problems are often formalized as Bi-Level optimizations (BLO). We introduce a novel perspective by turning a given BLO problem into a stochastic optimization, where the inner loss function becomes a smooth probability distribution, and the outer loss becomes an expected loss over the inner distribution. To solve this stochastic optimization, we adopt Stochastic Gradient Langevin Dynamics (SGLD) MCMC to sample inner distribution, and propose a recurrent algorithm to compute the MC-estimated hypergradient. Our derivation is similar to forward-mode differentiation, but we introduce a new first-order approximation that makes it feasible for large models without needing to store huge Jacobian matrices. The main benefits are two-fold: i) Our stochastic formulation takes into account uncertainty, which makes the method robust to suboptimal inner optimization or non-unique multiple inner minima due to overparametrization; ii) Compared to existing methods that often exhibit unstable behavior and hyperparameter sensitivity in practice, our method leads to considerably more reliable solutions. We demonstrate that the new approach achieves promising results on diverse meta learning problems and easily scales to learning 87M hyperparameters in the case of Vision Transformers
CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13
Get PDFA critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening paired with advanced computational and machine learning methods to each predict up to 100 inhibitory ligands. Across all teams, 1957 compounds were predicted and were subsequently procured from commercial catalogs for biophysical assays. Of these compounds, 0.7% were confirmed to bind to Nsp13 in a surface plasmon resonance assay. The six best performing computational workflows used fragment growing, active learning, or conventional virtual screening with and without complementary deep-learning scoring functions. Follow-up functional assays resulted in identification of two compound scaffolds that bound Nsp13 with a Kd below 10 µM and inhibited in vitro helicase activity. Overall, CACHE #2 participants were successful in identifying hit compound scaffolds targeting Nsp13, a central component of the coronavirus replication-transcription complex. Computational design strategies recurrently successful across the first two CACHE challenges include linking or growing docked or crystallized fragments and docking small and diverse libraries to train ultra-fast machine-learning models. The CACHE #2 competition reveals how crowd-sourcing ligand prediction efforts using a distinct array of approaches followed with critical biophysical assays can result in novel lead compounds to advance drug discovery efforts
Aortic valve replacement in patients with asymptomatic severe aortic stenosis: the devil is in the detail
Get PDFFor most patients with severe but asymptomatic aortic stenosis, current guidelines favor a conservative approach, with close surveillance for the development of symptoms, an unexplained decline in left ventricular systolic function or rapid hemodynamic progression. However, recent randomized data has reignited debate, including calls for these guidelines to be revised and aortic valve replacement (AVR) recommended for all patients with severe aortic stenosis, irrespective of symptoms. Before implementing any change, it is important that we carefully review the evidence, particularly that provided by the 4 recent randomized clinical trials. The Randomized Comparison of Early Surgery versus Conventional Treatment in Very Severe Aortic Stenosis (RECOVERY) and Aortic Valve Replacement Versus Conservative Treatment in Asymptomatic Severe Aortic Stenosis (AVATAR) trials both compared a conventional strategy of clinical surveillance with surgical AVR. In the RECOVERY trial, patients randomized to surgical AVR had a lower incidence of peri-operative or cardiovascular death after a median of 6 years. Likewise, in the AVATAR trial, surgical AVR reduced the risk of death, myocardial infarction, stroke, or heart failure hospitalization at both 3 and 5 years. Indeed, at 5 years, there was a lower risk of all-cause, but not cardiovascular, death. While these data are at face value compelling, it is important to acknowledge that both the RECOVERY and AVATAR trials were small and recruited highly selected populations, who were relatively young (mean age 64 and 67 years, respectively), at low-risk and with high transvalvular gradients.<br/
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