Few-Body States in Fermi-Systems and Condensation Phenomena

Residual interactions in many particle systems lead to strong correlations. A multitude of spectacular phenomenae in many particle systems are connected to correlation effects in such systems, e.g. pairing, superconductivity, superfluidity, Bose-Einstein condensation etc. Here we focus on few-body bound states in a many-body surrounding.Comment: 10 pages, proceedings 1st Asian-Pacific Few-Body Conference, needs fbssuppl.sty of Few-Body System

HDAC3 is essential for Human Leukemic Cell Growth and the Expression of β-catenin, MYC, and WT1

Therapy of acute myeloid leukemia (AML) is unsatisfactory. Histone deacetylase inhibitors (HDACi) are active against leukemic cells in vitro and in vivo. Clinical data suggest further testing of such epigenetic drugs and to identify mechanisms and markers for their efficacy. Primary and permanent AML cells were screened for viability, replication stress/DNA damage, and regrowth capacities after single exposures to the clinically used pan-HDACi panobinostat (LBH589), the class I HDACi entinostat/romidepsin (MS-275/FK228), the HDAC3 inhibitor RGFP966, the HDAC6 inhibitor marbostat-100, the non-steroidal anti-inflammatory drug (NSAID) indomethacin, and the replication stress inducer hydroxyurea (HU). Immunoblotting was used to test if HDACi modulate the leukemia-associated transcription factors beta-catenin, Wilms tumor (WT1), and myelocytomatosis oncogene (MYC). RNAi was used to delineate how these factors interact. We show that LBH589, MS-275, FK228, RGFP966, and HU induce apoptosis, replication stress/DNA damage, and apoptotic fragmentation of beta-catenin. Indomethacin destabilizes beta-catenin and potentiates anti-proliferative effects of HDACi. HDACi attenuate WT1 and MYC caspase-dependently and -independently. Genetic experiments reveal a cross-regulation between MYC and WT1 and a regulation of beta-catenin by WT1. In conclusion, reduced levels of beta-catenin, MYC, and WT1 are molecular markers for the efficacy of HDACi. HDAC3 inhibition induces apoptosis and disrupts tumor-associated protein expression

Neural basis of shame and guilt experience in women with borderline personality disorder.

Borderline personality disorder (BPD) is characterized by instability of affect, emotion dysregulation, and interpersonal dysfunction. Especially shame and guilt, so-called self-conscious emotions, are of central clinical relevance to BPD. However, only few experimental studies have focused on shame or guilt in BPD and none investigated their neurobiological underpinnings. In the present functional magnetic resonance imaging study, we took a scenario-based approach to experimentally induce feelings of shame, guilt, and disgust with neutral scenarios as control condition. We included 19 women with BPD (age 26.4 ± 5.8 years; DSM-IV diagnosed; medicated) and 22 healthy female control subjects (age 26.4 ± 4.6 years; matched for age and verbal IQ). Compared to controls, women with BPD reported more intense feelings when being confronted with affective scenarios, especially higher levels of shame, guilt, and fear. We found increased amygdala reactivity in BPD compared to controls for shame and guilt, but not for disgust scenarios (p = 0.05 FWE corrected at the cluster level; p < 0.0001 cluster defining threshold). Exploratory analyses showed that this was caused by a diminished habituation in women with BPD relative to control participants. This effect was specific to guilt and shame scenarios as both groups showed amygdala habituation to disgust scenarios. Our work suggests that heightened shame and guilt experience in BPD is not related to increased amygdala activity per se, but rather to decreased habituation to self-conscious emotions. This provides an explanation for the inconsistencies in previous imaging work on amygdala involvement in BPD as well as the typically slow progress in the psychotherapy of dysfunctional self-conscious emotions in this patient group

Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research

INTRODUCTION: Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. METHODS: Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. RESULTS: Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently (n = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application (n = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected (n = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis (n = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). CONCLUSION: TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science's translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the "AI chasm" continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice

Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research

2023 Hogg, Al-Zubaidy, Keane, Hughes, Beyer and Maniatopoulos.Introduction: Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. Methods: Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. Results: Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently (n = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application (n = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected (n = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis (n = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). Conclusion: TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science\u27s translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the “AI chasm” continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice

Experiments on two-phase flow in a vertical tube with a moveable obstacle

A novel technique to study the two-phase flow field around an asymmetric diaphragm in a vertical pipe is presented, that enables producing data for CFD code validation in complex geometries. Main feature is a translocation of the diaphragm to scan the 3D void field with a stationary wire-mesh sensor. Besides the measurement of time-averaged void fraction fields, a novel data evaluation method was developed to extract estimated liquid velocity profiles from the wire-mesh sensor data. The flow around an obstacle of the chosen geometry has many topological similarities with complex flow situations in bends, T-junctions, valves, safety valves and other components of power plant equipment and flow phenomena like curved stream lines, which form significant angles with the gravity vector, flow separation at sharp edges and recirculation zones in their wake are present. In order to assess the quality of the CFD code and their underlying multiphase flow and turbulence models pre-test calculations by ANSYS CFX 10.0 were carried out. A comparison between the calculation results and the experimental data shows a good agreement in term of all significant qualitative details of the void fraction and liquid velocity distributions. Furthermore, the report contains a method to assess the lateral components of bubble velocities in the form of a basic theoretical description and visualisation examples. The plots show the deviation of the flow around the obstacle in term of vectors represented the average velocities of the instantaneous cross-sections of all bubbles in the time interval when they pass the measuring plane. A detailed uncertainty analyse of the velocity assessments concludes the presented report. It includes remarks about the comparison with a second method for calculating bubble velocity profiles - the cross-correlation. In addition, this chapter gives an overview about the influence of acceleration and deceleration effects on the velocity estimation

Photodissociation of p-process nuclei studied by bremsstrahlung induced activation

A research program has been started to study experimentally the near-threshold photodissociation of nuclides in the chain of cosmic heavy element production with bremsstrahlung from the ELBE accelerator. An important prerequisite for such studies is good knowledge of the bremsstrahlung distribution which was determined by measuring the photodissociation of the deuteron and by comparison with model calculations. First data were obtained for the astrophysically important target nucleus 92-Mo by observing the radioactive decay of the nuclides produced by bremsstrahlung irradiation at end-point energies between 11.8 MeV and 14.0 MeV. The results are compared to recent statistical model calculations.Comment: 6 pages, 8 figures, Proceedings Nuclear Physics in Astrophysics II, May 16-20, 2005, Debrecen, Hungary. The original publication is available at www.eurphysj.or

Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence

BACKGROUND: Quantitative systematic reviews have identified clinical artificial intelligence (AI)-enabled tools with adequate performance for real-world implementation. To our knowledge, no published report or protocol synthesizes the full breadth of stakeholder perspectives. The absence of such a rigorous foundation perpetuates the "AI chasm," which continues to delay patient benefit. OBJECTIVE: The aim of this research is to synthesize stakeholder perspectives of computerized clinical decision support tools in any health care setting. Synthesized findings will inform future research and the implementation of AI into health care services. METHODS: The search strategy will use MEDLINE (Ovid), Scopus, CINAHL (EBSCO), ACM Digital Library, and Science Citation Index (Web of Science). Following deduplication, title, abstract, and full text screening will be performed by 2 independent reviewers with a third topic expert arbitrating. The quality of included studies will be appraised to support interpretation. Best-fit framework synthesis will be performed, with line-by-line coding completed by 2 independent reviewers. Where appropriate, these findings will be assigned to 1 of 22 a priori themes defined by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework. New domains will be inductively generated for outlying findings. The placement of findings within themes will be reviewed iteratively by a study advisory group including patient and lay representatives. RESULTS: Study registration was obtained from PROSPERO (CRD42021256005) in May 2021. Final searches were executed in April, and screening is ongoing at the time of writing. Full text data analysis is due to be completed in October 2021. We anticipate that the study will be submitted for open-access publication in late 2021. CONCLUSIONS: This paper describes the protocol for a qualitative evidence synthesis aiming to define barriers and facilitators to the implementation of computerized clinical decision support tools from all relevant stakeholders. The results of this study are intended to expedite the delivery of patient benefit from AI-enabled clinical tools. TRIAL REGISTRATION: PROSPERO CRD42021256005; https://tinyurl.com/r4x3thvp. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33145