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Automating the analysis of public saliency and attitudes toward biodiversity from digital media
Get PDFMeasuring public attitudes toward wildlife provides crucial insights into human relationships with nature and helps monitor progress toward Global Biodiversity Framework targets. Yet, conducting such assessments at a global scale presents challenges. Digital news and social media offer a rich record of public discourse, but extracting information about attitudes toward wildlife from these sources is not straightforward. Selecting effective search terms is complicated by differences between everyday names for taxa and their scientific or formal common names, and raw news and social media data are often cluttered with irrelevant content and syndicated articles. To address search term selection, we used a folk taxonomy approach that derives recognizable species groupings from shared common name endings. We identified syndicated articles by using cosine similarity on term frequency‐inverse document frequency vectors. To filter out irrelevant content while minimizing the need for corpus‐specific annotation and model training, we developed a 2‐stage relevance filter that uses unsupervised learning to reveal common topics and an open‐source zero‐shot large language model (LLM) to assign topics to article titles and estimate relevance. We conducted sentiment, topic, and volume analyses on the resulting data. To illustrate our method, we examined news and X posts containing search terms for bats, pangolins, elephants, and gorillas from 2019 through 2021, a period that covers the onset of the COVID‐19 pandemic. Up to 62% of articles containing bat search terms were unrelated to bats as wildlife, underscoring the importance of relevance filtering. News articles mentioning horseshoe bats, initially implicated in the outbreak, increased significantly in January 2020, with significant sentiment shifts in news and X posts mentioning horseshoe bats emerging later (October 2020). Our methods provide a practical application of modern, general‐purpose natural language processing (NLP) tools, including LLMs, for analyzing public perceptions of biodiversity relative to current events or conservation outreach and marketing campaigns
From polymers to rings and back again: catalytic recycling of waste oxygenated polymers
No full textThis thesis describes the chemical recycling to monomer of polycarbonates and polyesters. In particular, this thesis investigates different catalysts for the chemical recycling of CO2-derived polycarbonates and develops a new chemical recycling route for epoxide/anhydride derived polyesters. The chemical recycling of poly(L-lactide) blends to L-lactide is also explored. The poly(L-lactide) chemical recycling process is then evaluated by techno-economic analysis and life cycle assessment. This thesis explores the scale up of the poly(L-lactide) to L-lactide chemical recycling process and compares the economic and environmental impacts of the chemical recycling process to virgin polymer production
Nature-based solutions for climate adaptation in small island developing states: a systematic review
Get PDFIntroduction: Small Island Developing States (SIDS) are disproportionately affected by climate change, with impacts threatening their communities, ecosystems, and economies. Nature-based solutions (NbS) offer a promising approach to address these challenges, yet their effectiveness in SIDS remains poorly understood. Methods: We systematically reviewed 49 studies reporting 53 NbS interventions across 26 SIDS, coding intervention types, ecosystems, climate hazards, adaptation effectiveness, broader outcomes (social, ecological, economic, mitigation), and reported socio-ecological resilience mechanisms. Results: Nearly three-quarters of cases reported positive climate outcomes, though only half provided clear evidence, and fewer employed baselines, counterfactuals, or thresholds. Evidence was skewed toward croplands and agroforestry, while coastal ecosystems were underrepresented. Broader outcomes were mostly positive, but reporting on ecological and social resilience mechanisms was limited, equity considerations were largely absent, and formal economic appraisals and direct comparisons with non-NbS alternatives were scarce. Large geographic gaps were also evident, with more than half of SIDS unrepresented in the literature. Discussion: Overall, the evidence indicates that NbS can reduce climate risks in SIDS and deliver ‘triple wins’ for climate, biodiversity, and people, but decision confidence is constrained by uneven geographic coverage, agricultural bias, lack of counterfactuals and baselines, limited equity reporting, and scarce economic appraisal. Future research priorities include: (1) stronger representation of under-studied SIDS contexts, (2) greater focus on coastal and ocean-related NbS, (3) evidence linked to baselines and counterfactuals, (4) holistic, long-term monitoring and evaluation, (5) national- and regional-scale synthesis of grey literature, and (6) integration of equity and knowledge pluralism in NbS design and evaluation. These steps would help governments design, finance, and account for high-integrity NbS in NDCs, NAPs, adaptation investment plans, and disaster-risk strategies. Systematic Review Registration: https://osf.io/wcb68, identifier wcb68
Insomnia and progression to total joint replacement in hip (41 737) and knee pain (81 958): a prospective UK biobank cohort study
Get PDFObjective: Insomnia often co-exists with hip or knee pain and is associated with greater pain severity. However, there is limited evidence on whether insomnia contributes to progression to joint replacement. Using data from the UK Biobank, we tested whether symptoms of insomnia among people with hip or knee pain are associated with undergoing total hip or knee joint replacement surgery. Methods: UK Biobank data from participants with hip (n=41 737) or knee pain (n=81 958) in the past 3 months were included. Using self-reported baseline data, participants were classified as ‘never’, ‘sometimes’ or ‘usually’ having insomnia symptoms (ie, trouble falling asleep or waking in the night). We examined associations between baseline symptoms of insomnia and undergoing total hip or knee replacement surgery using adjusted Cox proportional hazards models. Results: In knee pain, ‘usually’ experiencing insomnia symptoms was associated with undergoing total knee replacement (adjusted HR 1.14 (95% CI 1.04 to 1.25)), within, but not beyond, 4.7 years of enrolment, compared with ‘never’ experiencing insomnia symptoms. No association was observed for ‘sometimes’ experiencing insomnia symptoms and total knee replacement among individuals with knee pain, nor for insomnia symptoms (‘usual’ or ‘sometimes’) and total hip replacement among individuals with hip pain. Conclusion: Insomnia may be a modifiable factor contributing to earlier progression to knee replacement. Targeting insomnia through interventions could form part of a holistic approach to managing chronic knee pain. Further research is needed to determine whether managing insomnia can reduce the risk of knee replacement surgery
Homer: Iliad Book XXIII
No full textDetailed analysis of language, structure, and narrative technique illuminates the brilliance of Homeric poetry, whilst the Introduction and Commentary explain and guide the reader through the text's literary and historical issues
Turning the tables: re-evaluating the incident between Jesus and the money-changers
No full textThis article will reassess the commonly called ‘Temple cleansing’ incident and its causes. This will be done by analyzing the event through a broader prism, including the historical context of the event, while taking into consideration commerce, culture, currency, and other factors in 30 CE Judea. This re-examination leads to the proposal that the familiar story’s past explanations and interpretations suffered from the different attempts to find a noble motive for the altercation with the money changers. A re-examination of the dispute suggests that the incident was over the currency Jesus presented to them for exchange, and that there was no noble purpose, or indeed no prior motive at all. This new interpretation and suggested narrative resolve the various contradictions in the Gospels, and this new reconstruction is consistent with the personality and circumstances of Jesus, as well as the realities of 30 CE. The article further suggests that the altercation with the money-changers belongs to an older text which depicted Jesus’ life as a historical figure
Mendelian Randomization With Longitudinal Exposure Data: Simulation Study and Real Data Application
Get PDFBackground and Aim: Mendelian randomization (MR) is a widely used tool to estimate causal effects using genetic variants as instrumental variables. MR is limited to cross‐sectional summary statistics of different samples and time points to analyze time‐varying effects. We aimed at using longitudinal summary statistics for an exposure in a multivariable MR setting and validating the effect estimates for the mean, slope, and within‐individual variability. Simulation Study: We tested our approach in 12 scenarios for power and type I error, depending on shared instruments between the mean, slope, and variability, and regression model specifications. We observed high power to detect causal effects of the mean and slope throughout the simulation, but the variability effect was low powered in the case of shared SNPs between the mean and variability. Mis‐specified regression models led to lower power and increased the type I error. Real Data Application: We applied our approach to two real data sets (POPS, UK Biobank). We detected significant causal estimates for both the mean and the slope in both cases, but no independent effect of the variability. However, we only had weak instruments in both data sets. Conclusion: We used a new approach to test a time‐varying exposure for causal effects of the exposure's mean, slope and variability. The simulation with strong instruments seems promising but also highlights three crucial points: (1) The difficulty to define the correct exposure regression model, (2) the dependency on the genetic correlation, and (3) the lack of strong instruments in real data. Taken together, this demands a cautious evaluation of the results, accounting for known biology and the trajectory of the exposure
Time trends in newly recorded diagnoses of 19 long term conditions before, during, and after the covid-19 pandemic: population based cohort study in England using OpenSAFELY
Get PDFObjective: To evaluate temporal changes in rates of newly recorded diagnoses for 19 long term conditions in England in relation to the covid-19 pandemic by disease, age group, sex, socioeconomic status, and ethnicity. Design: Population based cohort study. Setting: Primary care and hospital admission data, with the approval of NHS England. Participants: 29 995 025 individuals registered with general practices in England contributing data to the OpenSAFELY-TPP platform. Main outcome measures: Temporal trends in age and sex standardised incident and prevalent diagnosis rates for 19 long term conditions between 1 April 2016 and 30 November 2024. Differences between expected and observed diagnosis rates after the onset of the covid-19 pandemic were compared using seasonal autoregressive integrated moving-average models, based on modelled projections of expected rates from pre-pandemic patterns. Results: All 19 conditions showed a sharp decline in newly recorded diagnoses during the first year of the pandemic, followed by variable recovery. As of November 2024, cumulative reductions in diagnoses remained evident for conditions such as depression (734 800 (27.7%) fewer diagnoses than expected; 95% prediction interval (PI) 703 100 to 766 400), asthma (152 900 (16.4%) fewer diagnoses; 95% PI 137 500 to 168 300), chronic obstructive pulmonary disease (COPD) (90 100 (15.8%) fewer diagnoses; 95% PI 81 400 to 98 900), psoriasis (54 700 (17.1%) fewer diagnoses; 95% PI 50 100 to 59 200), and osteoporosis (54 100 (11.5%) fewer diagnoses; 95% PI 47 100 to 61 100). Conversely, diagnoses of chronic kidney disease have increased by 34.8% above expected levels during the pandemic recovery period, corresponding to 359 000 additional diagnoses (95% PI 333 500 to 384 500). Unadjusted subgroup analyses stratified by ethnicity and socioeconomic status indicated that, after an initial decrease, dementia diagnosis rates have risen above pre-pandemic levels for people of white ethnicity and in less deprived socioeconomic areas, but not for those from other ethnicities and more deprived areas. Conclusions: Since the covid-19 pandemic, there have been fewer diagnoses than expected for conditions such as depression, asthma, COPD, and osteoporosis, in contrast with a rapid increase in diagnoses of chronic kidney disease since 2022. Unadjusted analyses stratified by ethnicity and socioeconomic status suggest differential patterns of recovery, particularly for individuals with dementia. This study highlights the potential for near real time monitoring of disease epidemiology using routinely collected health data, informing strategies to enhance case detection and investigate inequities in healthcare
Increasing use of generative artificial intelligence by teenagers
Get PDFThe use of Generative Artificial Intelligence (GenAI) by teenagers is increasing rapidly. GenAI is a form of artificial intelligence that creates new text, images, video and audio, using models based on huge amounts of training data. However, using GenAI can also create misinformation and biased, inappropriate and harmful outputs. Teenagers are increasingly using GenAI in daily life, including in mental healthcare, and may not be aware of the limitations and risks. GenAI may also be used for malicious purposes that may have long-term, negative impacts on mental health. There is a need to increase awareness of how GenAI may have a negative impact on the mental health of teenagers
Are there lightning fires in the Brazilian Amazon?
Get PDFThe Brazilian Amazon contains approximately 40% of the world’s tropical rainforest and plays a critical role in preserving biodiversity and regulating water, energy and carbon cycles. However, deforestation and increasingly frequent droughts, heatwaves and wildfires threaten these rainforests. Amazonian fires are generally assumed to be entirely anthropogenic, which has led to lightning-ignited fires being underexplored. Here, we present the first detailed assessment of the spatiotemporal patterns of lightning-ignited fires in the Amazon rainforest to elucidate the role of lightning and human ignitions in shaping Amazon fire dynamics. To do this, we matched cloud-to-ground lightning strokes from the Global Lightning Dataset (GLD360) with individual fire events between 2019 and 2024 to obtain a probability of lightning ignition for each fire. We also calculated a human-ignition probability index using proximity to roads, waterways, and human land cover as proxies for human activity. By combining both probabilistic indices with ground-observed lightning ignitions from eight protected areas, we could optimize the threshold that determines if an ignition is more likely to be caused by lightning or human activities. We estimate that in the Brazilian Amazon, lightning caused on average 0.2%–0.4% of all fires each year (234–407 ignitions per year) and 1.1%–1.2% of the annually burned area (1226–1358 km2 per year) between 2019 and 2024. More than 89% of these fires occurred in the late dry season between August and November, peaking in September and October. Despite lightning-ignited fires contributing a small proportion of all Amazonian fires, they constitute over 25% of the fires in identified grid clusters in parts of the states of Pará (particularly in the Breves region), Amazonas, and Rondônia. This study provides the first estimation of the role of natural ignitions in Amazon fire dynamics and a scientific basis for understanding their contribution within the region