Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN

Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN’s unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN’s infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines

Gravitational Waves from the Dynamical Bar Instability in a Rapidly Rotating Star

A rapidly rotating, axisymmetric star can be dynamically unstable to an m=2 "bar" mode that transforms the star from a disk shape to an elongated bar. The fate of such a bar-shaped star is uncertain. Some previous numerical studies indicate that the bar is short lived, lasting for only a few bar-rotation periods, while other studies suggest that the bar is relatively long lived. This paper contains the results of a numerical simulation of a rapidly rotating gamma=5/3 fluid star. The simulation shows that the bar shape is long lived: once the bar is established, the star retains this shape for more than 10 bar-rotation periods, through the end of the simulation. The results are consistent with the conjecture that a star will retain its bar shape indefinitely on a dynamical time scale, as long as its rotation rate exceeds the threshold for secular bar instability. The results are described in terms of a low density neutron star, but can be scaled to represent, for example, a burned-out stellar core that is prevented from complete collapse by centrifugal forces. Estimates for the gravitational-wave signal indicate that a dynamically unstable neutron star in our galaxy can be detected easily by the first generation of ground based gravitational-wave detectors. The signal for an unstable neutron star in the Virgo cluster might be seen by the planned advanced detectors. The Newtonian/quadrupole approximation is used throughout this work.Comment: Expanded version to be published in Phys. Rev. D: 13 pages, REVTeX, 13 figures, 9 TeX input file

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware, at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware.Comment: 7 pages, 4 figures, submitted to APL Machine Learnin

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here, we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two-dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware

Distinct cortical and striatal actions of a β-arrestin-biased dopamine D2 receptor ligand reveal unique antipsychotic-like properties.

The current dopamine (DA) hypothesis of schizophrenia postulates striatal hyperdopaminergia and cortical hypodopaminergia. Although partial agonists at DA D2 receptors (D2Rs), like aripiprazole, were developed to simultaneously target both phenomena, they do not effectively improve cortical dysfunction. In this study, we investigate the potential for newly developed β-arrestin2 (βarr2)-biased D2R partial agonists to simultaneously target hyper- and hypodopaminergia. Using neuron-specific βarr2-KO mice, we show that the antipsychotic-like effects of a βarr2-biased D2R ligand are driven through both striatal antagonism and cortical agonism of D2R-βarr2 signaling. Furthermore, βarr2-biased D2R agonism enhances firing of cortical fast-spiking interneurons. This enhanced cortical agonism of the biased ligand can be attributed to a lack of G-protein signaling and elevated expression of βarr2 and G protein-coupled receptor (GPCR) kinase 2 in the cortex versus the striatum. Therefore, we propose that βarr2-biased D2R ligands that exert region-selective actions could provide a path to develop more effective antipsychotic therapies

Structural basis for Smoothened receptor modulation and chemoresistance to anticancer drugs

The Smoothened receptor (SMO) mediates signal transduction in the hedgehog pathway, which is implicated in normal development and carcinogenesis. SMO antagonists can suppress the growth of some tumors; however, mutations at SMO have been found to abolish their anti-tumor effects, a phenomenon known as chemoresistance. Here we report three crystal structures of human SMO bound to the antagonists SANT1 and Anta XV, and the agonist, SAG1.5, at 2.6–2.8Å resolution. The long and narrow cavity in the transmembrane domain of SMO harbors multiple ligand binding sites, where SANT1 binds at a deeper site as compared with other ligands. Distinct interactions at D4736.55 elucidated the structural basis for the differential effects of chemoresistance mutations on SMO antagonists. The agonist SAG1.5 induces a conformational rearrangement of the binding pocket residues, which could contribute to SMO activation. Collectively, these studies reveal the structural basis for the modulation of SMO by small molecules

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app

Improving care for people with dementia: development and initial feasibility study for evaluation of life story work in dementia care

Background: Improving dementia care quality is an urgent priority nationally and internationally. Life story work (LSW) is an intervention that aims to improve individual outcomes and care for people with dementia and their carers. LSW gathers information and artefacts about the person, their history and interests, and produces a tangible output: the ‘life story’. Objective: To establish whether or not full evaluation of LSW was feasible. Design: Mixed-methods feasibility study. Methods: In-depth interviews and focus groups explored experiences of LSW and best practice with people with dementia, family members and dementia care staff. A systematic review explored best practice and theories of change for LSW. These stages helped to identify the outcomes and resources to explore in the feasibility study. A representative sample survey of health and social care dementia care providers in England established LSW practice in different settings. A survey of a self-selected sample of family members of people with dementia explored how LSW is experienced. Two small outcome studies (stepped-wedge study in six care homes and pre-test post-test study in inpatient specialist dementia care wards) explored the feasibility of full evaluation of LSW in these settings. Settings: Survey: generalist and specialist care homes; NHS dementia care settings; and community dementia services. Feasibility study: care homes and NHS inpatient dementia care wards. Participants: NHS and social care services, people with dementia, family carers, care home staff and NHS staff. Interventions: LSW. Main outcome measures: Spread of LSW and good practice, quality of life (QoL) for the person with dementia and carers, relationships between people with dementia and family carers, staff attitudes about dementia, staff burnout, resource use and costs. Review methods: Narrative review and synthesis, following Centre for Review and Dissemination guidelines. Results: Good practice in LSW is identifiable, as are theories of change about how it might affect given outcomes. Indicators of best practice were produced. LSW is spreading but practice and use vary between care settings and are not always in line with identified good practice. Two different models of LSW are evident; these are likely to be appropriate at different stages of the dementia journey. The feasibility study showed some positive changes in staff attitudes towards dementia and, for some people with dementia, improvements in QoL. These may be attributable to LSW but these potential benefits require full evaluation. The feasibility work established the likely costs of LSW and highlighted the challenges of future evaluation in care homes and inpatient dementia care settings. Limitations: There was insufficient evidence in the literature to allow estimation of outcome size. We did not carry out planned Markov chain modelling to inform decisions about carrying out future evaluation because of the dearth of outcome data in the literature; low levels of data return for people with dementia in the hospital settings; lack of detected effect for most people with dementia; and questions about implementation in the research settings. Conclusions: LSW is used across different health and social care settings in England, but in different ways, not all of which reflect ‘good practice’. This large, complex study identified a wide range of challenges for future research, but also the possibility that LSW may help to improve care staff attitudes towards dementia and QoL for some people with dementia. Future work: Full evaluation of LSW as an intervention to improve staff attitudes and care is feasible with researchers based in or very close to care settings to ensure high-quality data collection. Funding: The National Institute for Health Research Health Services and Delivery Research programme. Keywords

Heavy-flavor production and medium properties in high-energy nuclear collisions --What next?

Open and hidden heavy-flavor physics in high-energy nuclear collisions are entering a new and exciting stage towards reaching a clearer understanding of the new experimental results with the possibility to link them directly to the advancement in lattice Quantum Chromo-Dynamics (QCD). Recent results from experiments and theoretical developments regarding open and hidden heavy-flavor dynamics have been debated at the Lorentz Workshop Tomography of the Quark-Gluon Plasma with Heavy Quarks, which was held in October 2016 in Leiden, The Netherlands. In this contribution, we summarize identified common understandings and developed strategies for the upcoming five years, which aim at achieving a profound knowledge of the dynamical properties of the quark-gluon plasma

Advancing drug discovery for schizophrenia

Sponsored by the New York Academy of Sciences and with support from the National Institute of Mental Health, the Life Technologies Foundation, and the Josiah Macy Jr. Foundation, "Advancing Drug Discovery for Schizophrenia" was held March 9-11 at the New York Academy of Sciences in New York City. The meeting, comprising individual talks and panel discussions, highlighted basic, clinical, and translational research approaches, all of which contribute to the overarching goal of enhancing the pharmaceutical armamentarium for treating schizophrenia. This report surveys work by the vanguard of schizophrenia research in such topics as genetic and epigenetic approaches; small molecule therapeutics; and the relationships between target genes, neuronal function, and symptoms of schizophrenia