Multiple myeloma as a challenging multidimensional random process: a data-driven web-based application for treatment selection
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The complex structure of aquatic food webs emerges from a few assembly rules
Food-web theory assumes that larger-bodied predators generally select larger prey. This allometric rule fails to explain a considerable fraction of trophic links in aquatic food webs. Here we show that food-web constraints result in guilds of predators that vary in size but have specialized on prey of the same size, and that the distribution of such specialist guilds explains about one-half of the food-web structure. We classified 517 pelagic species into five predator functional groups. Most of these follow three prey selection strategies: a guild following the allometric rule whereby larger predators eat larger prey and two guilds of specialists that prefer either smaller or larger prey than predicted by the allometric rule. Such coexistence of non-specialist and specialist guilds independent from taxa or body size points towards structural principles behind ecological complexity. We show that the pattern describes >90% of observed linkages in 218 food webs in 18 aquatic ecosystems worldwide. The pattern can be linked to eco-evolutionary constraints to prey exploitation and provides a blueprint for more effective food-web models.
Clinical practice recommendations for the diagnosis and management of X-linked hypophosphataemia
X-linked hypophosphataemia (XLH) is a rare metabolic bone disorder caused by pathogenic variants in the PHEX gene, which is predominantly expressed in osteoblasts, osteocytes and odontoblasts. XLH is characterized by increased synthesis of the bone-derived phosphaturic hormone fibroblast growth factor 23 (FGF23), which results in renal phosphate wasting with consecutive hypophosphataemia, rickets, osteomalacia, disproportionate short stature, oral manifestations, pseudofractures, craniosynostosis, enthesopathies and osteoarthritis. Patients with XLH should be provided with multidisciplinary care organized by a metabolic bone expert. Historically, these patients were treated with frequent doses of oral phosphate supplements and active vitamin D, which was of limited efficiency and associated with adverse effects. However, the management of XLH has evolved in the past few years owing to the availability of burosumab, a fully humanized monoclonal antibody that neutralizes circulating FGF23. Here, we provide updated clinical practice recommendations for the diagnosis and management of XLH to improve outcomes and quality of life in these patients.
Post-processing methods for delay embedding and feature scaling of reservoir computers
Reservoir computing is a machine learning method that is well-suited for complex time series prediction tasks. Both delay embedding and the projection of input data into a higher-dimensional space play important roles in enabling accurate predictions. We establish simple post-processing methods that train on past node states at uniformly or randomly-delayed timeshifts. These methods improve reservoir computer prediction performance through increased feature dimension and/or better delay embedding. Here we introduce the multi-random-timeshifting method that randomly recalls previous states of reservoir nodes. The use of multi-random-timeshifting allows for smaller reservoirs while maintaining large feature dimensions, is computationally cheap to optimise, and is our preferred post-processing method. For experimentalists, all our post-processing methods can be translated to readout data sampled from physical reservoirs, which we demonstrate using readout data from an experimentally-realised laser reservoir system.
Mobilizing power quality and reliability measurements for electricity equity and justice in Africa
In sub-Saharan Africa, urban electricity inequities manifesting as poor power quality and reliability (PQR) are prevalent. Yet, granular PQR data and frameworks for assessing PQR inequities and guiding equitable electricity interventions remain sparse. To address this gap, we present a conceptual framework that leverages energy justice, capability and multidimensional poverty theories alongside concepts relating to power systems to quantify PQR inequities in sub-Saharan Africa. To demonstrate our framework and using 1 year’s worth of remotely sensed PQR data from Accra, Ghana, we assessed the distributive scale of PQR inequities, explored how multidimensional poverty exacerbates these inequities and examined the impact of PQR on households’ domestic capabilities. We found wider patterns of PQR inequities and a link between poor PQR and neighbourhoods with higher multidimensional poverty. We conclude that using remotely sensed data combined with justice and capability frameworks offers a powerful method for revealing PQR inequities and driving sustainable energy transitions.
Dynamic optimizers for complex industrial systems via direct data-driven synthesis
The chemical process industry (CPI) faces significant challenges in improving sustainability and efficiency while maintaining conservative principles for managing cost, complexity, and uncertainty. This work introduces a data-driven approach to dynamic real-time optimization (D-RTO) that addresses the aforementioned concerns by directly extracting process optimization policies from historical plant data. Our method constructs a value function to evaluate trajectory quality and employs weighted regression to derive improved policies. When applied to a plant-wide industrial process control problem, the proposed optimizer demonstrates superior performance in adapting to disturbances while maintaining stability and product quality. These results challenge conventional assumptions regarding the potential of data-driven optimization in the CPI. Although limitations exist due to the black-box nature of neural networks, this study presents a promising avenue for enhancing operational efficiency in industrial settings. The proposed approach offers a practical solution for process optimization, as it leverages readily available historical data and does not require extensive modeling efforts. By demonstrating significant efficiency improvement on a realistic industrial benchmark problem, this work paves the way for the adoption of data-driven optimization techniques in real-world CPI applications.
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