Radio detection prospects for a bulge population of millisecond pulsars as suggested by Fermi LAT observations of the inner Galaxy

Analogously to globular clusters, the dense stellar environment of the Galactic center has been proposed to host a large population of as-yet undetected millisecond pulsars (MSPs). Recently, this hypothesis found support in the analysis of gamma rays from the inner Galaxy seen by the Large Area Telescope (LAT) aboard the Fermi satellite, which revealed a possible excess of diffuse GeV photons in the inner 15 deg about the Galactic center (Fermi GeV excess). The excess can be interpreted as the collective emission of thousands of MSPs in the Galactic bulge, with a spherical distribution that strongly peaks towards the Galactic center. In order to fully establish the MSP interpretation, it is essential to find corroborating evidence in multi-wavelength searches, most notably through the detection of radio pulsation from individual bulge MSPs. Based on globular cluster observations and the gamma-ray emission from the inner Galaxy, we investigate the prospects for detecting MSPs in the Galactic bulge. While previous pulsar surveys failed to identify this population, we demonstrate that, in the upcoming years, new large-area surveys with focus on regions a few degrees north or south of the Galactic center should lead to the detection of dozens of bulge MSPs. Additionally, we show that, in the near future, deep targeted searches of unassociated Fermi sources should be able to detect the first few MSPs in the bulge. The prospects for these deep searches are enhanced by a tentative gamma-ray/radio correlation that we infer from high-latitude gamma-ray MSPs. Such detections would constitute the first clear discoveries of field MSPs in the Galactic bulge, with far-reaching implications for gamma-ray observations, the formation history of the central Milky Way and strategy optimization for future radio observations.Comment: 24 pages, 17 figures, 5 tables. Minor clarifications. Matches version published in Ap

Beam lead technology

Beam lead technology for microcircuit interconnections with applications to metallization, passivation, and bondin

Nodular Thyroid Disease in the Era of Precision Medicine

Management of thyroid nodules in the era of precision medicine is continuously changing. Neck ultrasound plays a pivotal role in the diagnosis and several ultrasound stratification systems have been proposed in order to predict malignancy and help clinicians in therapeutic and follow-up decision. Ultrasound elastosonography is another powerful diagnostic technique and can be an added value to stratify the risk of malignancy of thyroid nodules. Moreover, the development of new techniques in the era of "Deep Learning," has led to a creation of machine-learning algorithms based on ultrasound examinations that showed similar accuracy to that obtained by expert radiologists. Despite new technologies in thyroid imaging, diagnostic surgery in 50-70% of patients with indeterminate cytology is still performed. Molecular tests can increase accuracy in diagnosis when performed on "indeterminate" nodules. However, the more updated tools that can be used to this purpose in order to "rule out" (Afirma GSC) or "rule in" (Thyroseq v3) malignancy, have a main limitation: the high costs. In the last years various image-guided procedures have been proposed as alternative and less invasive approaches to surgery for symptomatic thyroid nodules. These minimally invasive techniques (laser and radio-frequency ablation, high intensity focused ultrasound and percutaneous microwave ablation) results in nodule shrinkage and improvement of local symptoms, with a lower risk of complications and minor costs compared to surgery. Finally, ultrasound-guided ablation therapy was introduced with promising results as a feasible treatment for low-risk papillary thyroid microcarcinoma or cervical lymph node metastases

Two-photon excitation and relaxation of the 3d-4d resonance in atomic Kr

Two-photon excitation of a single-photon forbidden Auger resonance has been observed and investigated using the intense extreme ultraviolet radiation from the free electron laser in Hamburg. At the wavelength 26.9 nm (46 eV) two photons promoted a 3d core electron to the outer 4d shell. The subsequent Auger decay, as well as several nonlinear above threshold ionization processes, were studied by electron spectroscopy. The experimental data are in excellent agreement with theoretical predictions and analysis of the underlying multiphoton processes

Study of the Quantum Efficiency of CsI Photocathodes Exposed to Oxygen and Water Vapour

The operation of CsI photocathodes in gaseous detectors requires special attention to the purity of the applied gas mixtures.We have studied the influence of oxygen and water vapour contaminations on the performance of CsI photocathodes for theALICE HMPID RICH prototype. Measurements were done through comparison of Cherenkov rings obtained from beamtests. Increased levels of oxygen and water vapour did not show any effect on the performance. The results of this studyfound a direct application in the way of storing CsI photocathodes over long periods nad in particular in the shipment of theHMPID prototype from CERN to the STAR experiment at BNL. (Abstract only available,full text to follow

Bioinspired negatively charged calcium phosphate nanocarriers for cardiac delivery of MicroRNAs

Aim: To develop biocompatible and bioresorbable negatively charged calcium phosphate nanoparticles (CaP-NPs) as an innovative therapeutic system for the delivery of bioactive molecules to the heart. Materials & methods: CaP-NPs were synthesized via a straightforward one-pot biomineralization-inspired protocol employing citrate as a stabilizing agent and regulator of crystal growth. CaP-NPs were administered to cardiac cells in vitro and effects of treatments were assessed. CaP-NPs were administered in vivo and delivery of microRNAs was evaluated. Results: CaP-NPs efficiently internalized into cardiomyocytes without promoting toxicity or interfering with any functional properties. CaP-NPs successfully encapsulated synthetic microRNAs, which were efficiently delivered into cardiac cells in vitro and in vivo. Conclusion: CaP-NPs are a safe and efficient drug-delivery system for potential therapeutic treatments of polarized cells such as cardiomyocytes

Performance of large area CsI-RICH prototypes for ALICE at LHC

We present the performances of large area CsI-RICH prototypes obtained in single particle events. The differential quantum efficiency of the photocathodes has been deduced from Cherenkov rings by means of two different procedures: a direct measurement with a thin NaF radiator and a Monte Carlo based estimation for a C$_6$F$_{14}$ radiator. A factor of merit of 45 cm$^{-1}$ has been found for the typical detector configuration. Two angle reconstruction algorithms have been used and the different errors affecting the Cherenkov angle resolution have been estimated combining the analytical treatment and the Monte Carlo simulation. Also the dependence on radiator thickness, Cherenkov ring radius, chamber voltage and particle incidence angle has been studied

Current State-of-the-Art of AI Methods Applied to MRI

Di Noia, C., Grist, J. T., Riemer, F., Lyasheva, M., Fabozzi, M., Castelli, M., Lodi, R., Tonon, C., Rundo, L., & Zaccagna, F. (2022). Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics, 12(9), 1-16. [2125]. https://doi.org/10.3390/diagnostics12092125Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.publishersversionpublishe