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Habituation and decline of anti-predator behaviours in colobus monkeys in dog-dense suburban Kenya
No full textDomestic dogs (Canis familiaris) are an invasive species that can affect wildlife behaviour and contribute to species extinction. In Diani and Galu, southeastern Kenya, they injure or kill colobus monkeys (Colobus angolensis palliatus) more than the other monkey species in the area. This study investigated how arboreal colobus in these suburban areas adjust anti-predator strategies to a dog-dense environment. From May to July 2018, we conducted focal follows on two colobus groups (Group A: 103 h; Group B: 98.5 h) with home ranges overlapping with dogs and where previous dog-colobus attacks were witnessed. Dog-colobus interactions (⩽20 m from a dog) occurred in 2% of observation hours, with 70% of interactions involving dog predatory behaviours. The primary anti-predator behaviour was vigilance during the interactions and moving away rather than fleeing. When we mapped these interactions, the high-risk areas accounted for 12 and 13% of the total home ranges, respectively. Both groups spent about half their time foraging in these high-risk areas, did not stay high in the vegetation while there, and did not socialise or rest more frequently in the low-risk areas. A 1969 study of colobus vocalisations in the same area described colobus responding to dogs with vocalisations, threat displays, and fleeing - typical anti-predator behaviours of the genus. We conclude that over 55 years of exposure and habituation to dogs in a rapidly growing suburban environment have diminished anti-predator responses. Although frequently employed anti-predator behaviours would be energetically costly, reduced expression may ultimately increase colobus vulnerability to dog predation
What's next for the psychology of science rejection?
Get PDFThe last decade has seen a surge in research on science attitudes, trust in science, and science rejection. As a result, our understanding of the psychology of science rejection has substantially improved. This is important, because science rejection is a pernicious problem that can obstruct potential solutions to various pressing societal and environmental challenges. At the same time, this field of inquiry is limited in—at least—two important ways. First, much of the work conducted is descriptive in nature and not sufficiently guided by theory. Second, research has largely and disproportionately focused on a limited range of science domains, resulting in narrow and/or fuzzy conceptualizations and operationalizations of ‘science’. In this article, we argue that for the field to move forward it needs to pay more attention to theory and validity
Precision oncology through next generation sequencing in hepatocellular carcinoma
Get PDFHepatocellular carcinoma (HCC) is a primary liver cancer that originates from underlying inflammation, often associated with Hepatitis B virus (HBV) or Hepatitis C virus (HCV) infections. Despite the availability of treatments, there are high rates of tumour relapse due to the development of drug resistance in infected cells. Next-Generation Sequencing (NGS) plays a crucial role in overcoming this issue by sequencing both viral and host genomes to identify mutations and genetic heterogeneity. The knowledge gained from sequencing is then utilised to develop countermeasures against these mutants through different combination therapies. Advances in NGS have led to sequencing with higher accuracy and throughput, thereby enabling personalized and effective treatments. The purpose of this article is to highlight how NGS has contributed to precision medicine in HCC and the possible integration of artificial intelligence (AI) to bolster the advancement
Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence
Get PDFThe integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy, personalize treatment plans, and improve service delivery. This review provides a comprehensive overview of the current applications and future potential of AI in ophthalmology. AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. Furthermore, AI is being utilized to develop personalized treatment plans by analyzing large datasets to predict individual responses to therapies, thus optimizing patient outcomes and reducing healthcare costs. In surgical applications, AI-driven tools are enhancing the precision of procedures like cataract surgery, contributing to better recovery times and reduced complications. Additionally, AI-powered teleophthalmology services are expanding access to eye care in underserved and remote areas, addressing global disparities in healthcare availability. Despite these advancements, challenges remain, particularly concerning data privacy, security, and algorithmic bias. Ensuring robust data governance and ethical practices is crucial for the continued success of AI integration in ophthalmology. In conclusion, future research should focus on developing sophisticated AI models capable of handling multimodal data, including genetic information and patient histories, to provide deeper insights into disease mechanisms and treatment responses. Also, collaborative efforts among governments, non-governmental organizations (NGOs), and technology companies are essential to deploy AI solutions effectively, especially in low-resource settings
"Triazole-linked thiazolidinedione-Benzothiazole hybrids: Design and biological evaluation as AChE inhibitors"
No full textNovel 2,4-thiazolidinedione-benzothiazole-triazole hybrids (7a-7l) were designed and synthesized as therapeutic agents with pleotropic activity for Alzheimer's disease (AD). These compounds were evaluated for their acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitory activities. Compound 7k, exhibited exceptional AChE inhibition (IC₅₀ = 0.083 μM), while compound 7d, showed potent activity (IC₅₀ = 0.119 μM). Kinetic studies revealed that 7k was able to exert its action through mixed types of inhibition. Also, the anti-inflammatory potential of these lead compounds was assessed in LPS-stimulated RAW 264.7 macrophages. Both compounds demonstrated significant dose-dependent inhibition of key inflammatory mediators, including NO, TNF-α, IL-6, and IL-1β at non-cytotoxic concentrations (≤10 μM). Notably, compound 7k exhibited superior anti-inflammatory activity, achieving 92 % NO inhibition, 65 % TNF-α reduction, and 61.1 % IL-1β suppression at 10 μM. Moreover, compound 7k exerted neuroprotective activity against H O induced neurotoxicity in SH-Sy5y cell line leading to reduction in LDH, ROS levels and improving cell survival. Finally, compound 7k was able to prevent Aβ aggregation at IC = 5 μM. Molecular docking studies provided structural insights into the possible binding interactions of compounds 7d and 7k within the AChE active site. The stability and binding energies of compounds 7d and 7k complexed with AChE were assessed over 100 ns molecular dynamics simulations and compared with Donepezil. The MM/GBSA binding energy calculations indicated that compound 7k exhibited a higher affinity for AChE in comparison with compound 7d and Donepezil, with ΔG values of -46.1, -42.6, and - 24.0 kcal/mol, respectively. These findings suggest that these novel hybrid molecules represent promising multi-target therapeutic candidates for AD treatment, effectively addressing both cholinergic dysfunction and neuroinflammation
Entrepreneurial Alertness in Dynamic Environments: Mediating Pathways to Entrepreneurial Orientation and Performance
Get PDFEntrepreneurial orientation (EO) is critical for firms navigating dynamic environments, yet the mechanisms driving its development remain underexplored. This study examines the role of entrepreneurial alertness (EA) as a mediator linking environmental dynamism to EO and firm performance. We argue that whilst information acquisition reduces uncertainty, excessive focus on gathering information without adequate processing can lead to inefficiencies and missed opportunities. This imbalance may hinder the development of EO and adversely affect firm performance. Using data from 209 small and medium enterprises in Ghana, collected across multiple informants in two waves, our findings provide empirical support for the proposed model. The study contributes to the EO literature by demonstrating the relationship between information acquisition and processing in fostering EO and performance. It also cautions against the risks of overemphasizing one dimension at the expense of the other in dynamic environments. Additionally, we extend the conceptualization of EA by demonstrating that its dimensions operate through flexible, non‐linear pathways, enabling entrepreneurs to adapt their information‐processing strategies to the demands of dynamic environments
A Translocal Compromise: Adoption of Anti-corruption Reforms in East Timor
Get PDFThis article examines what happens when plural normative ideas and arrangements to address an issue reach local settings through transnational networks. Using anti-corruption reforms in East Timor as the lens, I show the diverse normative aspirations of international and local actors involved in transferring and receiving new regulatory arrangements. By proposing compromise as another possible outcome of transnational legal transfers, the study examines how compromises shape the scope and limits of adopted regulations. The anti-corruption reforms case study from East Timor allows us to identify when and how translocal compromises occur, who is part of such compromises, and how they influence the legal transfer and adoption process. Drawing on insights from comparative law, law and society, and regulation studies scholarship, the article provides a bottom-up perspective of transnational legal reforms, illustrating the entanglements of these initiatives with the local politics, conflicts, and power struggles. The findings underscore the need for more qualitative studies on legal transfers where multiple international and local actors are involved, capturing how their power struggles shape the scope and limits of regulatory arrangements that are ultimately adopted. By illustrating the interactions between local, national, and international actors, the article contributes to understanding the complexities, possibilities, and limits of transnational legal reform initiatives in specific contexts
Construct validity of measures of care home resident quality of life: cross-sectional analysis using data from a pilot Minimum Data Set in England
Get PDFBackground: To maintain good standards of care, evaluations of policy interventions or potential improvements to care are required. A number of quality of life (QoL) measures could be used but there is little evidence for England as to which measures would be appropriate. Using data from a pilot Minimum Data Set (MDS) for care home residents from the Developing resources And minimum dataset for Care Homes’ Adoption (DACHA) study, we assessed the discriminant construct validity of QoL measures, using hypothesis testing to assess the factors associated with QoL.
Methods: Care home records for 679 residents aged over 65 from 34 care homes were available that had been linked to health records and care home provider data. In addition to data on demographics, level of needs and impairment, proxy report social care-, capability- and health-related QoL of participants were completed (ASCOT-Proxy-Resident, ICECAP-O, EQ-5D-5L Proxy 2). Discriminant construct validity was assessed through testing hypotheses developed from previous research and QoL measure constructs. Multilevel regression models were analysed to understand how QoL was influenced by personal characteristics (e.g. sex, levels of functional and cognitive ability), care home level factors (type of home, level of quality) and resident use of health services (potentially avoidable emergency hospital admissions). Multiple imputation was used to address missing data.
Results: All three QoL measures had acceptable construct validity and captured different aspects of QoL, indicated by different factors explaining variation in each measure. All three measures were negatively associated with levels of cognitive impairment, whilst ICECAP-O and EQ-5D-5L Proxy 2 were negatively associated with low levels of functional ability. ASCOT-Proxy-Resident was positively associated with aspects of quality and care effectiveness at both resident- and care home-level.
Conclusion: The study found acceptable construct validity for ASCOT-Proxy-Resident, ICECAP-O and EQ-5D-5L Proxy 2 in care homes, with findings suggesting the three are complementary measures based on different constructs. The study has also provided evidence to support the inclusion of these QoL measures in any future MDS
Life events: A classification study and an exploration of consumer behaviour by cluster
No full textA life event is a major incident that changes the status or circumstances of a recipient, such as marriage, divorce, or the birth of a child. Life events tend to cause changes in consumer behaviour and provide companies with opportunities to respond to those changes. Much of the research into consumer behaviour is based on the cognitive perspective, but an important stream of research looks at situational influences. Life events are a form of situational influence that can impact consumer behaviour. This thesis addresses three key research questions, which are outlined below:
RQ1. What is known about life events with respect to consumer behaviour, and what remains unexplored and needs to be investigated further?
RQ2. What are the different characteristics of life events, and how could these characteristics be used to classify life events, identifying their commonalities and differences?
RQ3. What is the impact of different types of life events on consumer behaviour?
Consistent with these research questions, this thesis aims to contribute to the life events literature by first developing an in-depth and systematic understanding of the extant literature on life events with respect to consumer behaviour, which is a highly fragmented interdisciplinary area. Second, it aims to identify different characteristics of life events and use them to classify them where life events that share commonalities will be clustered and their differences and similarities highlighted. Third, it aims to determine the cluster-wise impact of life events on consumer behaviour by highlighting the effect of varying life events on consumer behaviour using the well-established Life Course Model (LCM).
Research question 1 was addressed through a Systematic Literature Review (SLR). After identifying the most suitable articles using the PRISMA framework, analysis was performed on the titles and abstracts of the articles using NVivo 14. The codes generated v through the auto-coding wizard were manually reworked and recoded to generate a meaningful platform for the identification of gaps in the literature. The clustering was then performed on the basis of coding similarities using Jaccard's coefficients. The SLR (chapter 2) highlighted six gaps in the literature, and the major gap identified was then addressed by the rest of the thesis (chapters 3-6).
Research question 2 was addressed by first refining and validating a list of life events through an expert panel study, where scholars in the field were surveyed about their perceptions of the list compiled by using two commonly used lists in the literature and adding life events that suit the modern lifestyles of the consumers. A list of thirty-six life events was validated and then used in the main study, where the classification was done through a systematic procedure. Clustering the life events was performed after developing the updated list of life events and using the important themes and characteristics of life events identified from the literature. A national survey in the UK was conducted where consumers were asked about their perceptions of the life events, they had experienced. The survey was informed by characteristics of life events found during the review, consumer traits and the LCM. The survey was designed using the Qualtrics platform and administered via a Toluna panel. The analysis was performed by presenting the data's demographic attributes and assessing the measures' reliability and validity prior to the cluster analysis. A hierarchical cluster analysis was then performed to classify life events, and MDS was used to generate geometric representation. This was followed by MANOVA, where attributes of each cluster were further studied by comparing means.
Research question 3 was then addressed by analysing the LCM for each cluster and illustrating its differences and similarities. The LCM predicts that life events affect consumer behaviours when mediated by variables that are based on three major perspectives, including normative, stress and human capital. The outcome variable of LCM is ‘change in consumption vi activities’. ‘Change in consumption activities’ was measured through two broad categories of consumption activities (leisure and necessities). This analysis was performed using mediation analysis (Model 4) by installing the PROCESS macro in IBM SPSS Statistics 27.
The findings of the analysis suggested that life events can be classified into four groups including ‘Riding the Swell: Unobtrusive, Low Impact Life Events’ (cluster 1), ‘Choppy Waters: Major Transitions and Midlife Melodramas’ (cluster 2), ‘Calmer Waters: Mixed Emotions and Anticipated Transitions’ (cluster 3), and ‘Rough seas: Personal and Financial Crises, Emotional Turmoil's (cluster 4). While determining the cluster-wise impact of life events using LCM, the finding revealed a consistent direct effect of the first two clusters on both forms of purchase. The varying direct effect of cluster 3 and cluster 4 on two forms of purchase can be used to foresee a change in consumer behaviour.
This thesis made several theoretical and methodological contributions. After highlighting several gaps through SLR that could advance research in the area, a major theoretical contribution is the classification of life according to its similarities and differences. Four clusters of distinctive groups were identified and described in terms of their key characteristics, LCM, and consumer traits. The findings contribute to the literature by explaining the varying effects of different life event groups on two major categories of purchases. In the future, researchers can use these findings to help design their studies and find out if this behaviour changes when other factors are included in the model. The life event characteristics used in this study were not empirically measured in past life event literature. The list of life events developed through expert judgment is an updated version for researchers who are concerned about life events that are particularly important to consumer behaviour. The prevalence of each life event in the data collection provides researchers with an estimate of the prevalence of each life event in the UK population. vii
The findings further have implications for policy and practice. The updated list of life events compiles the life events important for consumer purchase, and life events that are not crucial for consumer behaviour were sifted. The list could, therefore, be treated as a compilation of life events that are specifically useful for decision-makers within a business's marketing department. The classification performed in this study brings about an empirical understanding of the ways some life events share certain attributes. Practitioners can use these clusters to target customers. The cluster-wise effect of life events was determined on change in the purchase of leisure and necessities by using LCM as a framework. Managers can use the findings to understand customers better and target them based on the finding
Three-dimensional flame temperature reconstruction through adaptive segmentation-weighted non-negative least squares and light field imaging
Get PDFExisting flame temperature reconstruction algorithms experience significant performance degradation when subjected to radiation intensity noise interference, resulting in limited accuracy in low-temperature regions of flames with broad temperature distributions. We propose a three-dimensional (3D) flame temperature reconstruction algorithm by integrating adaptive segmentation-weighted non-negative least squares with light field imaging. Building on the non-negative least squares framework, the proposed algorithm introduces an adaptive strategy to improve the temperature reconstruction accuracy of low-temperature regions of flames. It also incorporates adaptive weight factors to reduce measurement errors caused by the extensive temperature range, enabling precise 3D temperature reconstruction. To validate the algorithm, numerical simulations of a bimodal asymmetric flame were performed to evaluate its noise tolerance and compare its performance with other existing algorithms. The simulation results indicated that the proposed algorithm demonstrates strong noise resistance, achieving ∼70% higher reconstruction accuracy than the least-square QR decomposition algorithm. Experiments were carried out on bimodal flames to reconstruct the temperature under various combustion conditions. The reconstructed temperature showed good agreement with trends reported in the literature. Our results demonstrate the viability and robustness of the proposed algorithm for reconstructing broader temperature distributions of flames
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