Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging

Biomedical photoacoustic (PA) imaging is typically used to exploit absorption-based contrast in soft tissue at depths of several centimeters. When it is applied to measuring PA waves generated in the brain, the acoustic properties of the skull bone cause not only strong attenuation but also a distortion of the wavefront, which diminishes image resolution and contrast. This effect is directly proportional to bone thickness. As a result, transcranial PA imaging in humans has been challenging to demonstrate. We measured the acoustic constraints imposed by the human skull to design an ultrasound sensor suitable for transcranial PA imaging and sensing. We imaged the phantoms using a planar Fabry-Perot sensor and employed a range of piezoelectric and optical ultrasound sensors to measure the frequency dependent acoustic transmission through human cranial bone. Transcranial PA images show typical frequency and thickness dependent attenuation and aberration effects associated with acoustic propagation through bone. The skull insertion loss measurements showed significant transmission at low frequencies. In comparison to conventional piezoelectric sensors, the performance of plano-concave optical resonator (PCOR) ultrasound sensors was found to be highly suitable for transcranial PA measurements. They possess high acoustic sensitivity at a low acoustic frequency range that coincides with the transmission window of human skull bone. PCOR sensors showed low noise equivalent pressures and flat frequency response which enabled them to outperform conventional piezoelectric transducers in transcranial PA sensing experiments. Transcranial PA sensing and imaging requires ultrasound sensors with high sensitivity at low acoustic frequencies, and a broad and ideally uniform frequency response. We designed and fabricated PCOR sensors and demonstrated their suitability for transcranial PA sensing

An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems

Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the open-world assumption in service-oriented systems. Deep RL was successfully applied to problems such as dynamic service composition, job scheduling, and offloading, as well as service adaptation. While Deep RL offers many benefits, understanding the decision-making of Deep RL is challenging because its learned decision-making policy essentially appears as a black box. Yet, understanding the decision-making of Deep RL is key to help service developers perform debugging, support service providers to comply with relevant legal frameworks, and facilitate service users to build trust. We introduce Chat4XAI to facilitate the understanding of the decision-making of Deep RL by providing natural-language explanations. Compared with visual explanations, the reported benefits of natural-language explanations include better understandability for non-technical users, increased user acceptance and trust, as well as more efficient explanations. Chat4XAI leverages modern AI chatbot technology and dedicated prompt engineering. Compared to earlier work on natural-language explanations using classical software-based dialogue systems, using an AI chatbot eliminates the need for eliciting and defining potential questions and answers up-front. We prototypically realize Chat4XAI using OpenAI's ChatGPT API and evaluate the fidelity and stability of its explanations using an adaptive service exemplar.Comment: To be published at 21st Int'l Conference on Service-Oriented Computing (ICSOC 2023), Rome, Italy, November 28-December 1, 2023, ser. LNCS, F. Monti, S. Rinderle-Ma, A. Ruiz Cortes, Z. Zheng, M. Mecella, Eds., Springer, 202

Photoacoustic pump-probe tomography of fluorophores in vivo using interleaved image acquisition for motion suppression

In fluorophores, the excited state lifetime can be modulated using pump-probe excitation. By generating photoacoustic (PA) signals using simultaneous and time-delayed pump and probe excitation pulses at fluences below the maximum permissible exposure, a modulation of the signal amplitude is observed in fluorophores but not in endogenous chromophores. This provides a highly specific contrast mechanism that can be used to recover the location of the fluorophore using difference imaging. The practical challenges in applying this method to in vivo PA tomography include the typically low concentrations of fluorescent contrast agents, and tissue motion. The former results in smaller PA signal amplitudes compared to those measured in blood, while the latter gives rise to difference image artefacts that compromise the unambiguous and potentially noise-limited detection of fluorescent contrast agents. To address this limitation, a method based on interleaved pump-probe image acquisition was developed. It relies on fast switching between simultaneous and time-delayed pump-probe excitation to acquire PA difference signals in quick succession, and to minimise the effects of tissue motion. The feasibility of this method is demonstrated in tissue phantoms and in initial experiments in vivo

A User Study on Explainable Online Reinforcement Learning for Adaptive Systems

Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty. Online RL facilitates learning from actual operational data and thereby leverages feedback only available at runtime. However, Online RL requires the definition of an effective and correct reward function, which quantifies the feedback to the RL algorithm and thereby guides learning. With Deep RL gaining interest, the learned knowledge is no longer explicitly represented, but is represented as a neural network. For a human, it becomes practically impossible to relate the parametrization of the neural network to concrete RL decisions. Deep RL thus essentially appears as a black box, which severely limits the debugging of adaptive systems. We previously introduced the explainable RL technique XRL-DINE, which provides visual insights into why certain decisions were made at important time points. Here, we introduce an empirical user study involving 54 software engineers from academia and industry to assess (1) the performance of software engineers when performing different tasks using XRL-DINE and (2) the perceived usefulness and ease of use of XRL-DINE.Comment: arXiv admin note: substantial text overlap with arXiv:2210.0593

Effect of microstructures on the electron-phonon interaction in the disordered metals Pd60_{60}Ag40_{40}

Using the weak-localization method, we have measured the electron-phonon scattering times $\tau_{ep}$ in Pd$_{60}$Ag$_{40}$ thick films prepared by DC- and RF-sputtering deposition techniques. In both series of samples, we find an anomalous $1/\tau_{ep} \propto T^2\ell$ temperature and disorder dependence, where $\ell$ is the electron elastic mean free path. This anomalous behavior cannot be explained in terms of the current concepts for the electron-phonon interaction in impure conductors. Our result also reveals that the strength of the electron-phonon coupling is much stronger in the DC than RF sputtered films, suggesting that the electron-phonon interaction not only is sensitive to the total level of disorder but also is sensitive to the microscopic quality of the disorder.Comment: accepted for publication in Phys. Rev.

a clinical study protocol

Introduction The approved analgesic and anti-inflammatory drugs ibuprofen and indometacin block the small GTPase RhoA, a key enzyme that impedes axonal sprouting after axonal damage. Inhibition of the Rho pathway in a central nervous system-effective manner requires higher dosages compared with orthodox cyclooxygenase-blocking effects. Preclinical studies on spinal cord injury (SCI) imply improved motor recovery after ibuprofen/indometacin-mediated Rho inhibition. This has been reassessed by a meta-analysis of the underlying experimental evidence, which indicates an overall effect size of 20.2% regarding motor outcome achieved after ibuprofen/indometacin treatment compared with vehicle controls. In addition, ibuprofen/indometacin may also limit sickness behaviour, non-neurogenic systemic inflammatory response syndrome (SIRS), neuropathic pain and heterotopic ossifications after SCI. Consequently, ‘small molecule’-mediated Rho inhibition after acute SCI warrants clinical investigation. Methods and analysis Protocol of an investigator-initiated clinical open-label pilot trial on high-dose ibuprofen treatment after acute traumatic, motor-complete SCI. A sample of n=12 patients will be enrolled in two cohorts treated with 2400 mg/day ibuprofen for 4 or 12 weeks, respectively. The primary safety end point is an occurrence of serious adverse events, primarily gastroduodenal bleedings. Secondary end points are pharmacokinetics, feasibility and preliminary effects on neurological recovery, neuropathic pain and heterotopic ossifications. The primary safety analysis is based on the incidence of severe gastrointestinal bleedings. Additional analyses will be mainly descriptive and casuistic. Ethics and dissemination The clinical trial protocol was approved by the responsible German state Ethics Board, and the Federal Institute for Drugs and Medical Devices. The study complies with the Declaration of Helsinki, the principles of Good Clinical Practice and all further applicable regulations. This safety and pharmacokinetics trial informs the planning of a subsequent randomised controlled trial. Regardless of the result of the primary and secondary outcome assessments, the clinical trial will be reported as a publication in a peer-reviewed journal. Trial registration number NCT02096913; Pre-results