The proteomic landscape of microglia in health and disease

Microglia are the resident immune cells of the central nervous system (CNS) and as such play crucial roles in regulating brain homeostasis. Their presence in neurodegenerative diseases is known, with neurodegeneration-associated risk genes heavily expressed in microglia, highlighting their importance in contributing to disease pathogenesis. Transcriptomics studies have uncovered the heterogeneous landscape of microglia in health and disease, identifying important disease-associated signatures such as DAM, and insight into both the regional and temporal diversity of microglia phenotypes. Quantitative mass spectrometry methods are ever increasing in the field of neurodegeneration, utilised as ways to identify disease biomarkers and to gain deeper understanding of disease pathology. Proteins are the main mechanistic indicators of cellular function, yet discordance between transcript and proteomic findings has highlighted the need for in-depth proteomic phenotypic and functional analysis to fully understand disease kinetics at the cellular and molecular level. This review details the current progress of using proteomics to define microglia biology, the relationship between gene and protein expression in microglia, and the future of proteomics and emerging methods aiming to resolve heterogeneous cell landscapes

Gaza war:if there’s a lesson from the Berlin airlift it’s that political will is required to avoid a humanitarian catastrophe

The desperate plight of the population of Gaza suggests that in this conflict humanitarian aid has become politicised. In 1948, when there was a clear-cut political consensus in the West that the people of Berlin must be helped in their hour of need, it was possible to mount and sustain such an enormous operation. To do so again with the people of Gaza will take the same political will. It’s not entirely clear, at least not yet, from the leaders of Israel’s western allies, that this political will exists. This is where a lesson can be drawn from Berlin, and it is a scandal that it is taking so long for this to happen

The nexus between national and regional reporting of economic news:Evidence from the United Kingdom and Scotland

Broadsheet newspapers are an important source of economic news. Using a unique dataset of more than489,000 articles over the last 20 years, this article asks the question whether newspapers published in Scotland communicate similar economic sentiments as UK-wide newspapers. The findings show that although Scottish and UK newspapers share a positive correlation, this relationship varies over time. There is evidenceof causality running mostly from the United Kingdom to Scotland. The Scottish Referendum 2014 has had animpact on newspaper reporting when there was more uncertainty in the communication. Individual newspapers respond differently during the referendum periods where some newspapers, The Daily Telegraph and Daily Record for instance reacted to the uncertainty rather strongly, whereas local newspapers represented news in a rather surprising positive note

Going the extra mile in face image quality assessment:A novel database and model

An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation. Although the study of face images is an important subfield in computer vision research, the lack of face IQA data and models limits the precision of current IQA metrics on face image processing tasks such as face superresolution, face enhancement, and face editing. To narrow this gap, in this paper, we first introduce the largest annotated IQA database developed to date, which contains 20,000 human faces &amp;#x2013; an order of magnitude larger than all existing rated datasets of faces &amp;#x2013; of diverse individuals in highly varied circumstances. Based on the database, we further propose a novel deep learning model to accurately predict face image quality, which, for the first time, explores the use of generative priors for IQA. By taking advantage of rich statistics encoded in well pretrained off-the-shelf generative models, we obtain generative prior information and use it as latent references to facilitate blind IQA. The experimental results demonstrate both the value of the proposed dataset for face IQA and the superior performance of the proposed model.</p

Teachers’ Use of Knowledge in Curriculum Making:Implications for Social Justice

Curriculum work is a key part of teachers’ practice and involves engaging with different types of knowledge. The way in which teachers use this knowledge will influence pupils’ experience of the curriculum in their classroom. In the globalised world of the 21st century, knowledge questions are important considerations, as schooling is situated in inequitable systems and social structures. This qualitative research study examined teachers’ use of knowledge as they made the curriculum in their classrooms. Data were generated via interviews with primary school teachers in Scotland and thematically analysed. Five types of knowledge were identified and then critically examined using Nancy Fraser’s framework for social justice. This enabled examination of the implications of teachers’ use of knowledge in their curriculum work. Findings were congruous with previous research on this topic, highlighting the complexity of curriculum work. Our analysis suggests that while the focus on ‘pupil-centred’ education is important, as it acts to recognise pupils in curriculum work, the redistribution of knowledge is a key consideration in the globalised and digitised present day. Digital tools and spaces not only provide access to information but also provide new opportunities for inequity and oppressive social relations; continual reflection on the knowledge flow into schools is an important consideration for both teachers and policy-makers.</p

Understanding and Bridging Gaps in the Use of Evidence from Modeling for Evidence-Based Policy Making in Nigeria’s Health System

Background. Modeled evidence is a proven useful tool for decision makers in making evidence-based policies and plans that will ensure the best possible health system outcomes. Thus, we sought to understand constraints to the use of models in making decisions in Nigeria’s health system and how such constraints can be addressed. Method. We adopted a mixed-methods study for the research and relied on the evidence to policy and Knowledge-to-Action (KTA) frameworks to guide the conceptualization of the study. An online survey was administered to 34 key individuals in health organizations that recognize modeling, which was followed by in-depth interviews with 24 of the 34 key informants. Analysis was done using descriptive analytic methods and thematic arrangements of narratives. Results. Overall, the data revealed poor use of modeled evidence in decision making within the health sector, despite reporting that modeled evidence and modelers are available in Nigeria. However, the disease control agency in Nigeria was reported to be an exception. The complexity of models was a top concern. Thus, suggestions were made to improve communication of models in ways that are easily comprehensible and to improve overall research culture within Nigeria’s health sector. Conclusion. Modeled evidence plays a crucial role in evidence-based health decisions. Therefore, it is imperative to strengthen and sustain in-country capacity to value, produce, interpret, and use modeled evidence for decision making in health. To overcome limitations in the usage of modeled evidence, decision makers, modelers/researchers, and knowledge brokers should forge viable relationships that regard and promote evidence translation

Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes

Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD &lt; HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasize the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.</p

The Craft Hub Journey:Project Catalogue

Introducing the Craft Hub project and the International Exhibition ‘Investigating Craft Practices across Europe’, including its journey across Europe, the artistic curation and set-up methodology for a replicable, accessible and sustainable design, adapting to seven unique exhibition spaces and content. The recurring themes, Heritage, Sustainability, Experimentation, Technological Innovation, Empowerment and Social Inclusion create common threads running through the activities and research carried out by each Craft Hub partner

Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging

Direct recycling of aluminum waste is crucial in sustainable manufacturing to mitigate environmental impact and conserve resources. This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. The experimental runs were designed using Full factorial and RSM via Minitab 21 software. RSM-ANN models were employed to examine the effect of factors and their interactions on response and to predict output, while GA-RSM and GA-ANN were used for optimization. The chips of different morphology were cold compressed into billet form and then hot forged. The effect of varying forging temperature (Tp, 450–550°C), holding time (HT, 60–120 minutes), and chip surface area to volume ratio (A&lt;jats:sub/&gt;S:V, 15.4–52.6 mm&lt;jats:sup/&gt;2/mm&lt;jats:sup/&gt;3) on ultimate tensile strength (UTS) was examined. Maximum UTS (237.4 MPa) was achieved at 550°C, 120 minutes and 15.4 mm&lt;jats:sup/&gt;2/mm&lt;jats:sup/&gt;3 of chip’s A&lt;jats:sub/&gt;S: V. The Tp had the largest contributing effect ratio on the UTS, followed by HT and A&lt;jats:sub/&gt;S:V according to ANOVA analysis. The proposed optimization process suggested 550°C, 60 minutes, and 15.4 mm&lt;jats:sup/&gt;2 as the optimal condition yielding the maximum UTS. The developed models’ evaluation results showed that ANN (with MSE = 1.48%) outperformed RSM model. Overall, the study promotes sustainable production by demonstrating the potential of integrating RSM and ML to optimize complex manufacturing processes and improve product quality