The Potential Role of a Hydrogen Network in Europe

Electricity transmission expansion has suffered many delays in Europe in recent decades, despite its significance for integrating renewable electricity into the energy system. A hydrogen network which reuses the existing fossil gas network could not only help to supply demand for low-emission fuels, but could also to balance variations in wind and solar energy across the continent and thus avoid power grid expansion. We pursue this idea by varying the allowed expansion of electricity and hydrogen grids in net-zero CO2 scenarios for a sector-coupled and self-sufficient European energy system with high shares of renewables. We cover the electricity, buildings, transport, agriculture, and industry sectors across 181 regions and model every third hour of a year. With this high spatio-temporal resolution, the model can capture bottlenecks in transmission networks, the variability of demand and renewable supply, as well as regional opportunities for the retrofitting of legacy gas infrastructure and the development of geological hydrogen storage. Our results show consistent system cost reductions with a pan-continental hydrogen network that connects regions with low-cost and abundant renewable potentials to demand centres, synthetic fuel production and cavern storage sites. Developing a hydrogen network reduces system costs by up to 26 billion Euros per year (3.4%), with the highest benefits when electricity grid reinforcements cannot be realised. Between 64% and 69% of this network could be built from repurposed natural gas pipelines. However, we find that hydrogen networks can only partially substitute for power grid expansion. While the expansion of both networks together can achieve the largest cost savings of 10%, the expansion of neither is truly essential as long as higher costs can be accepted and regulatory changes are made to manage grid bottlenecks.Comment: including supplementary materia

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemani

Trusted Brokers?:Identifying the Challenges Facing Data Centres

Research data centres (RDCs) in environmental science are currently facing challenges due to a number of factors. These include increased volume and heterogeneity of incoming data, transdisciplinary research, and a growing diversity of data consumers from academics through to private industry actors and governmental bodies. Many of these challenges relate to perceived trust in the data provided by the RDCs and in the data centres themselves. In this paper we explore these challenges and identify five distinct themes or ‘mechanisms’ (standardistation, supplementary information, interactivity, provenance and traceability, and the management of stakeholder interests). Using the lens of trust to situate these challenges in RDC practice, we discuss how these challenges and mechanisms relate to the emergence of new technologies such as blockchain. We report that there are many benefits that blockchain technology can have in RDC brokerage and data management, and in fostering trust in data centres by data producers and consumers. However we also note that this technology can also have unintended consequences, impacting upon the trust held by stakeholders. We conclude that trust is an appropriate construct for combating the challenges that RDCs face, but that in order to effectively design and implement these mechanisms, care should be taken with the underlying and often implicit intricacies. We recommend that these intricacies should be mapped out and planned before implementing technology, and that future work will upon this

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson's disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS

Enhanced stability and local structure in biologically relevant amorphous materials containing pyrophosphate

There is increasing evidence that amorphous inorganic materials play a key role in biomineralisation in many organisms, however the inherent instability of synthetic analogues in the absence of the complex in vivo matrix limits their study and clinical exploitation. To address this, we report here an approach that enhances long-term stability to >1 year of biologically relevant amorphous metal phosphates, in the absence of any complex stabilisers, by utilising pyrophosphates (P2O7 4-); species themselves ubiquitous in vivo. Ambient temperature precipitation reactions were employed to synthesise amorphous Ca2P2O7.nH2O and Sr2P2O7.nH2O (3.8 < n < 4.2) and their stability and structure were investigated. Pair distribution functions (PDF) derived from synchrotron X-ray data indicated a lack of structural order beyond ~8 A° in both phases, with this local order found to resemble crystalline analogues. Further studies, including 1H and 31P solid state NMR, suggest the unusually high stability of these purely inorganic amorphous phases is partly due to disorder in the P–O–P bond angles within the P2O7 units, which impede crystallization, and to water molecules, which are involved in H-bonds of various strengths within the structures and hamper the formation of an ordered network. In situ high temperature powder X-ray diffraction data indicated that the amorphous nature of both phases surprisingly persisted to ~450° C. Further NMR and TGA studies found that above ambient temperature some water molecules reacted with P2O7 anions, leading to the hydrolysis of some P–O–P linkages and the formation of HPO4 2- anions within the amorphous matrix. The latter anions then recombined into P2O7 ions at higher temperatures prior to crystallization. Together, these findings provide important new materials with unexplored potential for enzyme-assisted resorption and establish factors crucial to isolate further stable amorphous inorganic materials

Synthesis of Organosilicon Ligands for Europium (III) and Gadolinium (III) as Potential Imaging Agents

The relaxivity of MRI contrast agents can be increased by increasing the size of the contrast agent and by increasing concentration of the bound gadolinium. Large multi-site ligands able to coordinate several metal centres show increased relaxivity as a result. In this paper, an “aza-type Michael” reaction is used to prepare cyclen derivatives that can be attached to organosilicon frameworks via hydrosilylation reactions. A range of organosilicon frameworks were tested including silsesquioxane cages and dimethylsilylbenzene derivatives. Michael donors with strong electron withdrawing groups could be used to alkylate cyclen on three amine centres in a single step. Hydrosilylation successfully attached these to mono-, di-, and tri-dimethylsilyl-substituted benzene derivatives. The europium and gadolinium complexes were formed and studied using luminescence spectroscopy and relaxometry. This showed the complexes to contain two bound water moles per lanthanide centre and T1 relaxation time measurements demonstrated an increase in relaxivity had been achieved, in particular for the trisubstituted scaffold 1,3,5-tris((pentane-sDO3A)dimethylsilyl)benzene-Gd3. This showed a marked increase in the relaxivity (13.1 r1p/mM−1s−1)

Real-world evidence for coverage determination of treatments for rare diseases

Health technology assessment (HTA) decisions for pharmaceuticals are complex and evolving. New rare disease treatments are often approved more quickly through accelerated approval schemes, creating more uncertainties about clinical evidence and budget impact at the time of market entry. The use of real-world evidence (RWE), including early coverage with evidence development, has been suggested as a means to support HTA decisions for rare disease treatments. However, the collection and use of RWE poses substantial challenges. These challenges are compounded when considered in the context of treatments for rare diseases. In this paper, we describe the methodological challenges to developing and using prospective and retrospective RWE for HTA decisions, for rare diseases in particular. We focus attention on key elements of study design and analyses, including patient selection and recruitment, appropriate adjustment for confounding and other sources of bias, outcome selection, and data quality monitoring. We conclude by offering suggestions to help address some of the most vexing challenges. The role of RWE in coverage and pricing determination will grow. It is, therefore, necessary for researchers, manufacturers, HTA agencies, and payers to ensure that rigorous and appropriate scientific principles are followed when using RWE as part of decision-making