Decoupling a Cooper-pair box to enhance the lifetime to 0.2 ms

We present a circuit QED experiment in which a separate transmission line is used to address a quasi-lumped element superconducting microwave resonator which is in turn coupled to an Al/AlO$_{x}$/Al Cooper-pair box (CPB) charge qubit. In our measurements we find a strong correlation between the measured lifetime of the CPB and the coupling between the qubit and the transmission line. By monitoring perturbations of the resonator's 5.44 GHz resonant frequency, we have measured the spectrum, lifetime ($T_{1}$), Rabi, and Ramsey oscillations of the CPB at the charge degeneracy point while the CPB was detuned by up to 2.5 GHz . We find a maximum lifetime of the CPB was $T_{1} = 200\ \mu$s for $f = 4$ to 4.5 GHz. Our measured $T_{1}$'s are consistent with loss due to coupling to the transmission line, spurious microwave circuit resonances, and a background decay rate on the order of $5\times 10^{3}$ s$^{-1}$ of unknown origin, implying that the loss tangent in the AlO$_{x}$ junction barrier must be less than about $4\times 10^{-8}$ at 4.5 GHz, about 4 orders of magnitude less than reported in larger area Al/AlO$_{x}$/Al tunnel junctions

Heart applications of 4D flow

Four-dimensional (4D) flow sequences are an innovative type of MR sequences based upon phase contrast (PC) sequences which are a type of application of Angio-MRI together with the Time of Flight (TOF) sequences and Contrast-Enhanced Magnetic Resonance Acquisition (CE-MRA). They share the basic principles of PC, but unlike PC sequences, 4D flow has velocity encoding along all three flow directions and three-dimensional (3D) anatomic coverage. They guarantee the analysis of flow with multiplanarity on a post-processing level, which is a unique feature among MR sequences. Furthermore, this technique provides a completely new level to the in vivo flow analysis as it allows measurements in never studied districts such as intracranial applications or some parts of the heart never studied with echo-color-doppler, which is its sonographic equivalent. Furthermore, this technique provides a completely new level to the in vivo flow analysis as it allows accurate measurement of the flows in different districts (e.g., intracranial, cardiac) that are usually studied with echo-color-doppler, which is its sonographic equivalent. Of note, the technique has proved to be affected by less inter and intra-observer variability in several application. 4D-flow basic principles, advantages, limitations, common pitfalls and artefacts are described. This review will outline the basis of the formation of PC image, the construction of a 4D-flow and the huge impact the technique is having on the cardiovascular non-invasive examination. It will be then studied how this technique has had a huge impact on cardiovascular examinations especially on a central heart level

Large collective Lamb shift of two distant superconducting artificial atoms

Virtual photons can mediate interaction between atoms, resulting in an energy shift known as a collective Lamb shift. Observing the collective Lamb shift is challenging, since it can be obscured by radiative decay and direct atom-atom interactions. Here, we place two superconducting qubits in a transmission line terminated by a mirror, which suppresses decay. We measure a collective Lamb shift reaching 0.8% of the qubit transition frequency and exceeding the transition linewidth. We also show that the qubits can interact via the transmission line even if one of them does not decay into it.Comment: 7+5 pages, 4+2 figure

Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?

In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development

Cardiac computed tomography radiomics: an emerging tool for the non-invasive assessment of coronary atherosclerosis

In the last decades, significant advances have been made in the preventive approaches to cardiovascular disease. Even so, coronary artery disease remains one of the main causes of morbidity and mortality worldwide. Invasive imaging modalities, such as intravascular ultrasound or optical coherence tomography, have played a key role in the comprehension of the pathological processes underlying myocardial infarction and cerebrovascular disease. These imaging techniques have contributed greatly to the identification and phenotyping of the culprit lesion, the so-called vulnerable plaque. Coronary computed tomographic angiography (CCTA) has emerged in more recent years as the non-invasive modality of choice in the study of coronary atherosclerosis, showing in many studies a diagnostic yield comparable to invasive approaches. Moreover, being able to describe extra-luminal characteristics of the affected vessel, CCTA has greatly contributed towards shifting the attention of researchers from the mere quantification of luminal stenosis to the identification of adverse plaque features, which appear to have a stronger prognostic value. However, the identification of some of the hallmarks of vulnerable plaques is qualitative in nature and, therefore, subject to some degree of inter-reader variability. Moreover, CCTA is still unable to identify some fine markers of plaque vulnerability which can be detected by invasive techniques, such as neovascularization and plaque erosion, among others. Nonetheless, radiological images can be viewed as vast 3-D datasets which, via the use of recent technology, allow for the extraction of numerous quantitative features that may be used to accurately phenotype a given lesion. Radiomics is the process of extrapolating innumerable parameters from a given region of interest, with the goal of establishing correlations between quantitative variables and clinical data. These datasets can then be manipulated to create predictive models via the use of automated algorithms in a process called machine learning. As a result of these approaches, radiological images may offer information regarding the characterization of a plaque which can go much beyond the boundaries of what can be qualitatively asserted by the human eye, contributing to expanding the knowledge of the disease and ultimately assist clinical decisions. Thus far, radiomics has found its more consistent area of application in the field of oncology; to present date, the amount of clinical data regarding coronary artery disease is still relatively small, partly due to the technical difficulties associated with the implementation of such techniques to the study of a small and geometrically complex lesion such as the coronary plaque. The present review, after a summary of the imaging modalities most commonly used nowadays in the study of coronary plaques, will provide a perspective on the application of radiomic analysis to coronary artery disease

"Does anyone even notice us?" COVID-19’s impact on academics’ well-being in a developing country

In March 2020, the President of South African announced that the nation would go into full lockdown in the wake of an increase in COVID-19 infections. Academics had, in some instances, only one day to prepare for “emergency remote teaching”. Few academics had taught online before, as South Africa’s internet connectivity is not guaranteed in underprivileged areas, where 80 per cent of the population reside. The online move thus necessitated an entirely novel pedagogy for most academics, with high potential for an escalation of work-related stress and related illness, outcomes we have related in the wider sphere of workplace readjustment during COVID-19, to a state of “pandemia”. In this article, we report on an institutional case study where we surveyed n=136 academics from a university in the Western Cape, South Africa to learn more about impacts of COVID-19 on their work. The data analysis adopts Ryff’s (1995) theory of well-being. Findings indicate that the enforced lockdown due to COVID-19 and the subsequent move to online teaching has had a negative impact on academics’ sense of well-being. However, the emergence of positive, caring relationships between colleagues is reported as a significant outcome of the COVID-19 enforced move to online teaching

Relationship between white matter hyperintensities volume and the circle of Willis configurations in patients with carotid artery pathology

Purpose We aimed to assess if there is a difference of distribution and volume of white matter hyperintensities (WMH) in the brain according to the Circle of Willis (CoW) configuration in patients with carotid artery pathology. Material and methods One-hundred consecutive patients (79 males, 21 females; mean age 70 years; age range 46–84 years) that underwent brain MRI before carotid endarterectomy (CEA) were included. FLAIR-WMH lesion volume was performed using a semi-automated segmentation technique and the status of the circle of Willis was assessed by two neuroradiologists in consensus. Results We found a prevalence of 55% of variants in the CoW configuration; 22 cases had one variants (40%); 25 cases had two variants (45.45%) and 8 cases showed 3 variants (14.55%). The configuration that was associated with the biggest WMH volume and number of lesions was the A1 + PcoA + PcoA. The PcoA variants were the most prevalent and there was no statistically significant difference in number of lesions and WMH for each vascular territory assessed and the same results were found for AcoA and A1 variants. Conclusion Results of our study suggest that the more common CoW variants are not associated with the presence of an increased WMH or number of lesions whereas uncommon configurations, in particular when 2 or more segment are missing increase the WMH volume and number of lesions. The WHM volume of the MCA territory seems to be more affected by the CoW configuration

GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization

Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor