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Bioenergetics and lipid metabolism in Alzheimer's disease:From cell biology to systemic health
No full textAlzheimer's disease (AD) is a complex neurodegenerative disorder characterized by progressive cognitive decline. Although amyloid-β and tau pathologies remain central to our understanding of AD, growing evidence suggests that disrupted lipid metabolism and impaired bioenergetics are closely linked to these hallmark features. Genetic, lipidomic and functional studies point to alterations in cholesterol, phospholipids and polyunsaturated fatty acids, which can influence mitochondrial function, organelle communication and glial responses. These processes are further modulated by apolipoprotein E (APOE) genotype, sex differences and systemic metabolic states such as obesity and diabetes, contributing to neuroinflammation and cognitive decline. Although findings are sometimes conflicting, an emerging theme is that lipid and energy metabolisms are central to how genetic and environmental risk factors shape AD pathogenesis. This integrated perspective highlights lipid and bioenergetic pathways as promising therapeutic targets, where metabolic modulators, lipid-directed interventions and lifestyle strategies may complement amyloid-based therapies and offer opportunities for precision approaches, particularly in women and APOE ε4 carriers.</p
Sparse outlier-robust PCA for multi-source data
No full textSparse and outlier-robust principal component analysis (PCA) has been a very active field of research recently. Yet, most existing methods apply PCA to a single data set whereas multi-source data—i.e. multiple related data sets requiring joint analysis—arise across many scientific areas. We introduce a novel PCA methodology that simultaneously (i) selects important features, (ii) allows for the detection of global sparse patterns across multiple data sources as well as local source-specific patterns, and (iii) is resistant to outliers. To this end, we develop a regularization problem with a penalty that accommodates global-local structured sparsity patterns, and where an outlier-robust covariance estimator, namely the ssMRCD, is used as plug-in to permit joint, robust analysis across multiple data sources. We provide an efficient implementation of our proposal via the alternating direction method of multipliers and illustrate its practical advantages in simulations and in applications
Investigating shared cognitive traits of autism spectrum disorder and picky eating
No full textObjective: Picky eating is common among children with autism spectrum disorder (ASD) and can lead to nutritional deficiencies with negative health consequences. Because ASD traits and picky eating often co-occur, it remains unclear whether similar cognitive mechanisms underlie picky eating in typically developing children. This study examined whether cognitive traits associated with ASD are also linked to picky eating in typically developing children. It was hypothesized that higher levels of picky eating would be associated with lower cognitive flexibility, less developed theory of mind (ToM), a local processing bias, and higher parent-reported ASD traits. Methods: A cross-sectional study was conducted with 198 children aged 4-6. The children completed three tasks to measure cognitive flexibility (Dimensional Change Card Sort), ToM, and global-local processing preferences. Receptive vocabulary was measured to control for general cognitive ability. Parents completed the Autism Spectrum Quotient (AQ-10) and Child Food Rejection Scale (CFRS) to assess ASD traits and picky eating, respectively. Results: Contrary to expectations, no significant relationship was found between picky eating and the cognitive traits studied. However, a small negative correlation was found between ToM and the picky eating subscale, suggesting that higher picky eating levels may be related to lower ToM abilities. Conclusion: These findings suggest that in typically developing children, ASD-related traits are probably not strongly associated with picky eating, with the exception of ToM. Future research is needed to examine if social factors appear to play a more crucial role in picky eating
Unbalanced Data Supported by Federated Learning with Uncertainty by Different Aggregation Methods
No full textFederated learning enables multiple clients to collaboratively train a machine learning model while keeping their local data private-omitting sharing data, making it a privacy-preserving technique. Data plays a key role in these models. However, in some cases, a single organization may not have enough data or high-quality data to build a reliable model, especially in a rapidly changing environment. In horizontal federated learning, each organization/client continuously refines its model, which is periodically fused and distributed among all participating clients in the federation for further enhancement. The fusion/aggregation process typically relies on a weighted averaging approach, where the weights are determined by the quality of each client’s model. This study explores approaches by using federated learning with respect to uncertainty and examines various aggregation strategies based on the performance of local models
Dynamic Conformal Prediction for Multi-Target Regression:Optimising Informational Efficiency under Joint Validity
No full textInductive conformal prediction equips point regressors with finite-sample prediction sets that provably contain the unknown label with prescribed probability. For multi-target regression, joint coverage across all output dimensions can be guaranteed by combining one-dimensional conformal predictors, one for each output dimension, resulting in an axis-aligned hyperrectangular prediction region. The validity and informational efficiency of these hyperrectangular prediction regions depend on the choice of the targeted error rate for the individual one-dimensional conformal predictors. We cast this choice as an error-budget allocation problem and introduce Dynamic Conformal Prediction for Multi-Target Regression (DCP-MT), a method that finds the budget allocation which minimises the hyperrectangles’ volumes while retaining joint coverage under exchangeability. Experiments on synthetic and real-world data sets demonstrate that DCP-MT reduces hyperrectangle volumes compared to state-of-the-art methods when nonconformity scores’ correlations across target dimensions are weak or heterogeneous, while maintaining the nominal coverage. The proposed method thus offers a simple, drop-in solution for existing multi-target regression pipelines
Semi-automatic deviation identification:The way forward to assurance over financial information
No full textFrom evidence to everyday care:Implementing and evaluating a lifestyle-focused approach in mental healthcare
No full textKids menu:Factors affecting children’s vegetable acceptance, sweet preference and loss of control eating
No full textAssessing the composition of myofibrillar and muscle connective protein fractions within skeletal muscle tissue
No full textABSTRACT (195 WORDS): It has been suggested that different nutritional stimuli are required to augment myofibrillar versus muscle connective protein synthesis rates. To study such different aspects of skeletal muscle remodeling, researchers often isolate myofibrillar or connective protein fractions from muscle tissue samples. However, the composition of these muscle protein fractions remains poorly defined. Here, we evaluated the amino acid profiles and protein compositions of the myofibrillar and muscle connective protein fractions within skeletal muscle tissue. The muscle connective protein fraction was shown to contain ∼70% of the total mixed muscle collagen content, with 4.4 ± 0.9% collagen relative to total protein content. This was 3–4 fold greater than the collagen content in mixed muscle tissue (1.2 ± 0.2%; p < 0.05). Myofibrillar proteins, such as actin and myosin, accounted for 39% of the myofibrillar protein fraction and 32% of the muscle connective protein fraction. The muscle connective protein fraction contained a higher proportion (42%) of key scaffolding proteins compared to the myofibrillar protein fraction (11%). In conclusion, the muscle connective protein fraction contains an enriched proportion of collagen among a large proportion of intra- and extracellular scaffolding and cell adhesion proteins, all of which are far less abundant in the myofibrillar protein fraction.</p
Cultural Diversity in Academic Motivation: Universality and Model Complexity
No full textThis contribution centres on two interconnected conjectures. The first posits that motivational—and the way we model what influences motivation— can be significantly enhanced by integrating the concept of growth orientation. This integration enables to connect growth mindset frameworks and motivational theories through straightforward antecedent-consequence models. The second conjecture is that simpler models tend to vary less and are more stable across different groups compared to more complex models. An example brings these two conjectures together by analysing cultural differences in academic motivation. It compares three models using data from international students studying mathematics and statistics: (1) a comprehensive growth orientation model incorporating both global and specific factors, estimated using Bifactor Exploratory Structural Equation Modelling (B-ESEM); (2) a simplified version of this model with growth orientation as the sole antecedent factor; and (3) a Structural Equation Model (SEM) using specific factors as antecedents. In the illustrative example, the degree of cultural diversity in motivational levels is found to be relatively small, comparable in magnitude to gender differences. However, more complex antecedent-consequence models can easily lead to the conclusion that significant diversity exists between cultural groups. Our findings challenge common assumptions about the cultural specificity of academic motivation models and highlight a promising yet often overlooked factor for building robust explanations of learning motivation: students’ growth orientation