Application of Optimal Control to CPMG Refocusing Pulse Design

We apply optimal control theory (OCT) to the design of refocusing pulses suitable for the CPMG sequence that are robust over a wide range of B0 and B1 offsets. We also introduce a model, based on recent progress in the analysis of unitary dynamics in the field of quantum information processing (QIP), that describes the multiple refocusing dynamics of the CPMG sequence as a dephasing Pauli channel. This model provides a compact characterization of the consequences and severity of residual pulse errors. We illustrate the methods by considering a specific example of designing and analyzing broadband OCT refocusing pulses of length 10 t180 that are constrained by the maximum instantaneous pulse power. We show that with this refocusing pulse, the CPMG sequence can refocus over 98% of magnetization for resonance offsets up to 3.2 times the maximum RF amplitude, even in the presence of +/- 10% RF inhomogeneity.Comment: 23 pages, 10 figures; Revised and reformatted version with new title and significant changes to Introduction and Conclusions section

Generation of 1.5 Million Beam Loss Threshold Values

CERN's Large Hadron Collider will store an unprecedented amount of energy in its circulating beams. Beamloss monitoring (BLM) is, therefore, critical for machine protection. It must protect against the consequences (equipment damage, quenches of superconducting magnets) of excessive beam loss. About 4000 monitors will be installed at critical loss locations. Each monitor has 384 beam abort thresholds associated; for 12 integrated loss durations ($40\mu$s to 83 s) and 32 energies (450GeV to 7 TeV). Depending on monitor location, the thresholds vary by orders of magnitude. For simplification, the monitors are grouped in 'families'. Monitors of one family protect similar magnets against equivalent loss scenarios. Therefore, they are given the same thresholds. The start-up calibration of the BLM system is required to be within a factor of five in accuracy; and the final accuracy should be a factor of two. Simulations (backed-up by control measurements) determine the relation between the BLM signal, the deposited energy and the critical energy deposition for damage or quench (temperature of the coil). The paper presents the strategy of determining 1.5 million threshold values

Model‐based control of mechanical ventilation: design and clinical validation

Background. We developed a model‐based control system using end‐tidal carbon dioxide fraction (FE′CO2) to adjust a ventilator during clinical anaesthesia. Methods. We studied 16 ASA I-II patients (mean age 38 (range 20-59) yr; weight 67 (54-87) kg) during i.v. anaesthesia for elective surgery. After periods of normal ventilation the patients were either hyper‐ or hypoventilated to assess precision and dynamic behaviour of the control system. These data were compared with a previous group where a fuzzy‐logic controller had been used. Responses to different clinical events (invalid carbon dioxide measurement, limb tourniquet release, tube cuff leak, exhaustion of carbon dioxide absorbent, simulation of pulmonary embolism) were also noted. Results. The model‐based controller correctly maintained the setpoint. No significant difference was found for the static performance between the two controllers. The dynamic response of the model‐based controller was more rapid (P<0.05). The mean rise time after a setpoint increase of 1 vol% was 313 (sd 90) s and 142 (17) s for fuzzy‐logic and model‐based control, respectively, and after a 1 vol% decrease was 355 (127) s and 177 (36) s, respectively. The new model‐based controller had a consistent response to clinical artefacts. Conclusion. A model‐based FE′CO2 controller can be used in a clinical setting. It reacts appropriately to artefacts, and has a better dynamic response to setpoint changes than a previously described fuzzy‐logic controller. Br J Anaesth 2004; 92: 800-

Horn fragments of the Halpern-Shoham Interval Temporal Logic

We investigate the satisfiability problem for Horn fragments of the Halpern-Shoham interval temporal logic depending on the type (box or diamond) of the interval modal operators, the type of the underlying linear order (discrete or dense), and the type of semantics for the interval relations (reflexive or irreflexive). For example, we show that satisfiability of Horn formulas with diamonds is undecidable for any type of linear orders and semantics. On the contrary, satisfiability of Horn formulas with boxes is tractable over both discrete and dense orders under the reflexive semantics and over dense orders under the irreflexive semantics but becomes undecidable over discrete orders under the irreflexive semantics. Satisfiability of binary Horn formulas with both boxes and diamonds is always undecidable under the irreflexive semantics

A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy.

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines

The medical threat of mamba envenoming in sub-Saharan Africa revealed by genus-wide analysis of venom composition, toxicity and antivenomics profiling of available antivenoms

Mambas (genus Dendroaspis) are among the most feared of the medically important elapid snakes found in sub-Saharan Africa, but many facets of their biology, including the diversity of venom composition, remain relatively understudied. Here, we present a reconstruction of mamba phylogeny, alongside genus-wide venom gland transcriptomic and high-resolution top-down venomic analyses. Whereas the green mambas, D. viridis, D. angusticeps, D. j. jamesoni and D. j. kaimosae, express 3FTx-predominant venoms, black mamba (D. polylepis) venom is dominated by dendrotoxins I and K. The divergent terrestrial ecology of D. polylepis compared to the arboreal niche occupied by all other mambas makes it plausible that this major difference in venom composition is due to dietary variation. The pattern of intrageneric venom variability across Dendroaspis represented a valuable opportunity to investigate, in a genus-wide context, the variant toxicity of the venom, and the degree of paraspecific cross-reactivity between antivenoms and mamba venoms. To this end, the immunological profiles of the five mamba venoms were assessed against a panel of commercial antivenoms generated for the sub-Saharan Africa market. This study provides a genus-wide overview of which available antivenoms may be more efficacious in neutralising human envenomings caused by mambas, irrespective of the species responsible. The information gathered in this study lays the foundations for rationalising the notably different potency and pharmacological profiles of Dendroaspis venoms at locus resolution. This understanding will allow selection and design of toxin immunogens with a view to generating a safer and more efficacious pan-specific antivenom against any mamba envenomation

A joint physics and radiobiology DREAM team vision - towards better response prediction models to advance radiotherapy

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines. [Abstract copyright: Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

Induction of effective and antigen-specific antitumour immunity by a liposomal ErbB2/HER2 peptide-based vaccination construct

Efficient delivery of tumour-associated antigens to appropriate cellular compartments of antigen-presenting cells is of prime importance for the induction of potent, cell-mediated antitumour immune responses. We have designed novel multivalent liposomal constructs that co-deliver the p63–71 cytotoxic T Lymphocyte epitope derived from human ErbB2 (HER2), and HA307–319, a T-helper (Th) epitope derived from influenza haemagglutinin. Both peptides were conjugated to the surface of liposomes via a Pam3CSS anchor, a synthetic lipopeptide with potent adjuvant activity. In a murine model system, vaccination with these constructs completely protected BALB/c mice from subsequent s.c. challenge with ErbB2-expressing, but not ErbB2-negative, murine renal carcinoma (Renca) cells, indicating the induction of potent, antigen-specific immune responses. I.v. re-challenge of tumour-free animals 2 months after the first tumour cell inoculation did not result in the formation of lung tumour nodules, suggesting that long-lasting, systemic immunity had been induced. While still protecting the majority of vaccinated mice, a liposomal construct lacking the Th epitope was less effective than the diepitope construct, also correlating with a lower number of CD8+ IFN-γ+ T-cells identified upon ex vivo peptide restimulation of splenocytes from vaccinated animals. Importantly, in a therapeutic setting treatment with the liposomal vaccines resulted in cures in the majority of tumour-bearing mice and delayed tumour growth in the remaining ones. Our results demonstrate that liposomal constructs which combine Tc and Th peptide antigens and lipopeptide adjuvants can induce efficient, antigen-specific antitumour immunity, and represent promising synthetic delivery systems for the design of specific antitumour vaccines

Characterization of the PTW 34031 ionization chamber (PMI) at RCNP with high energy neutrons ranging from 100 – 392 MeV

Radiation monitoring at high energy proton accelerators poses a considerable challenge due to the complexity of the encountered stray radiation fields. These environments comprise a wide variety of different particle types and span from fractions of electron-volts up to several terra electron-volts. As a consequence the use of Monte Carlo simulation programs like FLUKA is indispensable to obtain appropriate field-specific calibration factors. At many locations of the LHC a large contribution to the particle fluence is expected to originate from high-energy neutrons and thus, benchmark experiments with mono-energetic neutron beams are of high importance to verify the aforementioned detector response calculations. This paper summarizes the results of a series of benchmark experiments with quasi mono-energetic neutrons of 100, 140, 200, 250 and 392 MeV that have been carried out at RCNP - Osaka University, during several campaigns between 2006 and 2014

A joint physics and radiobiology DREAM team vision - towards better response prediction models to advance radiotherapy

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team’s consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines