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Machine learning-integrated and fingerprint-based similarity search against immuno oncology library for identification of novel ERK2 inhibitors
No full textThe extracellular signal-regulated kinase 2 (ERK2) protein plays a pivotal role in regulating cell division cycles and signaling pathways essential for various biological processes. ERK2 inhibition is a promising therapeutic approach for diseases like cardiovascular deformities, neurodegenerative disorders, and other forms of cancers. The current study presents novel compounds potentially inhibiting ERK2 activity, thus disrupting its cellular functions. A thorough structural assessment of the available crystallographic information was undertaken. The protein’s active site was deciphered, and the experimental grid space of inhibitors interaction was allocated. The study proceeded further with a precise inhibitor search employing a “similarity search” algorithm based on the previously reported kinase inhibitors. Schematic virtual screening method combined with molecular docking steps were executed to enlist the probable hits. AI/ML-based pharmacokinetics properties helped streamline hits’ initial chemical space and select the most potent leads. Complexes formed by these compounds were analyzed for their stability by molecular dynamics (MD) simulations. Post dynamics statistical calculations, viz., protein backbone and ligand RMSD, the radius of gyration, and the constitutive amino acids fluctuations (RMSF), confirmed the protein–ligand association over a period of 300 ns. The magnitude of co-ordinations was estimated by intermolecular H-bond count and the MMGBSA calculations. The free energy landscape (FEL) and principal component analysis (PCA) demonstrated the thermodynamical feasibility of the complex formation with an affinity greater than the previously reported inhibitors. This study, thus, presents a promising avenue for advancing the drug discovery process by identifying novel ERK2 protein inhibitors with potential benefits for healthcare.<br/
A novel multi-modal Federated Learning based thermal-aware job scheduling framework
No full textCooling costs constitute more than half of the total data center energy expenditure. Thermal imbalance results in hotspot regions requiring additional cooling power. To reduce it, thermal aware job scheduling is a well-known software solution that is subject to predicting correct server temperatures. Existing solutions have not explored intelligent solutions and rely only on logic based algorithms to allocate tasks that work on predefined rules. Few deep learning based solutions that are proposed, have not explored its alternatives and existing data modalities in data centers, resulting in inefficient models. Existing literature only proposes solutions based on unimodal tabular data. Therefore, we propose a multimodal architecture that considers different underlying data modalities in data centers to increase the model's efficiency and predict correct server temperatures. The increasing production of data and the need for storage and processing units has led to the development of distributed data centers. Existing techniques are limited to individual data centers which fail to consider the data privacy restrictions that arise while dealing with distributed scenarios. Findings from our simulations affirm our proposed scheme in terms of the objectives mentioned above. We propose a federated learning architecture that efficiently deals with distributed data centers while ensuring privacy. Our simulation results show an overall increase in the efficiency of the model in comparison to an existing intelligent solution. Furthermore, we provide comparative results that show how our model performs better and achieves lower thermal imbalance as compared to an existing scheme.</p
Spin and silence: royal penance and Carolingian propaganda
Get PDFSince the pioneering work of Mayke de Jong, many historians have studied the political use of penance in the Carolingian empire. This article explores how eighth- and ninth-century texts turned demands for penance and penitential acts by members of the royal family into political propaganda, and changes in such approaches. The earlier Carolingians preferred to remain silent about their own sins, while making limited use of penitential punishment for rivals. Louis the Pious’ more ambitious use of self-confession for Christian exaltation was initially successful, but the rise of rival camps of propagandists led to this confession later being turned against him. By 840, there was a new reluctance by rulers and others to admit culpability, reflected in the rise of the “non-confession confession”, in which penitential tropes were used without any specific personal fault being admitted. Lothar II in the 860s made ingenious attempts to harness penitential discourse to support his divorce and remarriage. His claims show a keen awareness of the possibilities and pitfalls of public confession, but he was finally unable to counter his opponents’ arguments. By the 870s, the development of widespread legal-penitential expertise paradoxically led to a new royal penitential silence, in which no ruler would publicly confess to any offence, however notoriou
Creating space for meaningful physical activity at home: women’s stories of social interaction, micro-adventure, and the joy of feeling strong
Get PDFDuring COVID-19 stay-at-home orders and social distancing, the home, garden and local spaces became focal points for physical activity (PA). These restrictions may have influenced the meaningfulness of PA. This paper draws on feminist perspectives on space and the body alongside the concept of meaningful PA to examine women’s PA at home towards the end of pandemic restrictions. In this visual ethnographic project, 11 women who were physically active at home each engaged in photo diaries and two online interviews for a retrospective and in-the-moment exploration of their PA at home during and after social distancing. Analysis considered the changing and subjective nature of meaningfulness in these contexts. Three composite vignettes are presented, titled ‘Everything is on your own terms’, ‘Expanding the four walls’, and ‘A micro-adventure all by myself’. These written and visual stories illuminate meaningful PA at a time shaped by reactions to stay-at-home orders and changing (gendered) relations to the home as a leisure, domestic, and work space. At-home PA was variously a compromise and a personally relevant choice. Participants found meaning in adapting PA to create the right challenge for them and expressed joy in developing physical strength. Digital and home PA spaces helped women to challenge normative PA practices while fostering different forms of social interaction. Constructions of meaningful PA are dynamic and socially situated in resistance to lockdown and loss of access of nature. The personal relevance of PA is affected by personal values and histories, and broader discourses of space and the body
A privacy-preserving approach to effectively utilize distributed data for malaria image detection
Get PDFMalaria is one of the life-threatening diseases caused by the parasite known as Plasmodium falciparum, affecting the human red blood cells. Therefore, it is an important to have an effective computer-aided system in place for early detection and treatment. The visual heterogeneity of the malaria dataset is highly complex and dynamic, therefore higher number of images are needed to train the machine learning (ML) models effectively. However, hospitals as well as medical institutions do not share the medical image data for collaboration due to general data protection regulations (GDPR) and the data protection act (DPA). To overcome this collaborative challenge, our research utilised real-time medical image data in the framework of federated learning (FL). We have used state-of-the-art ML models that include the ResNet-50 and DenseNet in a federated learning framework. We have experimented both models in different settings on a malaria dataset constituting 27,560 publicly available images and our preliminary results showed that the DenseNet model performed better in accuracy (75%) in contrast to ResNet-50 (72%) while considering eight clients, while the trend was observed as common in four clients with the similar accuracy of 94%, and six clients showed that the DenseNet model performed quite well with the accuracy of 92%, while ResNet-50 achieved only 72%. The federated learning framework enhances the accuracy due to its decentralised nature, continuous learning, and effective communication among clients, as well as the efficient local adaptation. The use of federated learning architecture among the distinct clients for ensuring the data privacy and following GDPR is the contribution of this research work.</p
Malaria vaccine efficacy, safety, and community perception in Africa: a scoping review of recent empirical studies
Get PDFAim: The review summarizes the recent empirical evidence on the efficacy, safety, and community perception of malaria vaccines in Africa. Methods: Academic Search Complete, African Journals Online, CINAHL, Medline, PsychInfo, and two gray literature sources were searched in January 2023, and updated in June 2023. Relevant studies published from 2012 were included. Studies were screened, appraised, and synthesized in line with the review aim. Statistical results are presented as 95% Confidence Intervals and proportions/percentages. Results: Sixty-six (N = 66) studies met the inclusion criteria. Of the vaccines identified, overall efficacy at 12 months was highest for the R21 vaccine (N = 3) at 77.0%, compared to the RTS,S vaccine (N = 15) at 55%. The efficacy of other vaccines was BK-SE36 (11.0–50.0%, N = 1), ChAd63/MVA ME-TRAP (− 4.7–19.4%, N = 2), FMP2.1/AS02A (7.6–9.9%, N = 1), GMZ2 (0.6–60.0%, N = 5), PfPZ (20.0–100.0%, N = 5), and PfSPZ-CVac (24.8–33.6%, N = 1). Injection site pain and fever were the most common adverse events (N = 26), while febrile convulsion (N = 8) was the most reported, vaccine-related Serious Adverse Event. Mixed perceptions of malaria vaccines were found in African communities (N = 17); awareness was generally low, ranging from 11% in Tanzania to 60% in Nigeria (N = 9), compared to willingness to accept the vaccines, which varied from 32.3% in Ethiopia to 96% in Sierra Leone (N = 15). Other issues include availability, logistics, and misconceptions. Conclusion: Malaria vaccines protect against malaria infection in varying degrees, with severe side effects rarely occurring. Further research is required to improve vaccine efficacy and community involvement is needed to ensure successful widespread use in African communities.</p
Nursing interventions for people who use new psychoactive substances
Get PDFThe use of new psychoactive substances is a growing concern across healthcare services in the UK. To date, more than 1,000 types of new psychoactive substances have been identified and they have a wide range of effects, potency and mechanisms of action, which can result in overdose and death. This article reviews the challenges experienced by nurses including in identifying new psychoactive substances, their associated risks and various psychosocial and pharmacological interventions. Currently, evidence surrounding the appropriate nursing interventions required for the misuse of new psychoactive substances is limited. Further research and training opportunities are required for nurses to manage service users who present having taken new psychoactive substances, particularly in hospital, substance misuse and mental health settings
Ethical implementation of artificial intelligence in the service industries
No full textThis study employs a systematic literature review (SLR) combined with bibliometric analysis to investigate the ethical implementation of Artificial Intelligence (AI) in the service industries. This research uncovers key challenges such as privacy, bias, transparency, and accountability, emphasizing the critical need for ethical AI practices in service sectors handling sensitive customer data. Findings reveal that AI’s ethical implementation is crucial in areas like decision support, customer engagement, automation, and new service development. The analysis provides actionable insights into enablers, including ethical guidelines, human oversight, comprehensive training, and adaptive organizational culture, which are essential for unlocking AI’s potential and mitigating risks. The study offers a roadmap for future research, advocating interdisciplinary collaboration, customer co-creation in ethical frameworks, and sector-specific policy adaptation, ultimately aiming to build responsible and trustworthy AI in the service industries
Push-pull determinants of livelihood diversification among rural dwellers in oil-polluted communities in Niger Delta, Nigeria
Get PDFLivelihood diversification enables households to participate in multiple activities to widen income sources. This research examined the determinants (push and pull) of livelihood diversification among the rural poor in oil-polluted communities of the Niger Delta. Primary data were used for the study using a well-structured questionnaire from 320 household heads who had a direct impact by the oil spill in Ogoni land. The data collected were analyzed using standard deviation, mean and paired sample tests. The study revealed that the push factors were strong motivation for diversification as the oil spillage in the area was enough distress and necessitated the diversification. However, some households were motivated by pull factors based on available skills to explore off-farm and non-farm activities. It is recommended that training initiatives aimed at equipping rural residents of oil-polluted areas with skills applicable to non-farm occupations should be consistently carried out
The grief cycle: investigating the influence of cycling on grief outcomes in individuals who have experienced a bereavement
Get PDFBackground: There is a lack of research that investigates the influence of physical activity on grief outcomes. This research aimed to examine the influence of cycling on grief outcomes in individuals who have experienced a bereavement. Method: Semi-structured interviews with 14 participants (n = 8 males; age M = 47.5 years) who engaged in cycling behaviour and had experienced a bereavement. Reflexive thematic analysis was used to guide analysis. Results: Four key themes were generated, providing: an 1) Embodied experience of cycling, within the 2) Cycling community, helping to provide support, alongside the 3) Nature connectedness, which led to 4) Post traumatic growth, following bereavement. Conclusion: Evidence suggest that cycling can provide an opportunity for a physical challenge, an immense connection to nature and a community of support from likeminded individuals. These therapeutic qualities of cycling should be considered for future interventions and adds novel findings to the area of cycling, bereavement and grief
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