Study of RPC gas mixtures for the ARGO-YBJ experiment

The ARGO-YBJ experiment consists of a RPC carpet to be operated at the Yangbajing laboratory (Tibet, P.R. China), 4300 m a.s.l., and devoted to the detection of showers initiated by photon primaries in the energy range 100 GeV - 20 TeV. The measurement technique, namely the timing on the shower front with a few tens of particles, requires RPC operation with 1 ns time resolution, low strip multiplicity, high efficiency and low single counting rate. We have tested RPCs with many gas mixtures, at sea level, in order to optimize these parameters. The results of this study are reported.Comment: 6 pages, 3 figures. To be published in Nucl. Instr. Meth. A, talk given at the "5th International Workshop on RPCs and Related Detectors", Bari (Italy) 199

A general estimator of the primary cosmic ray energy with the ARGO-YBJ experiment

The determination of the primary cosmic ray all-particle spectrum with ground-based air shower experiments usually depends on the assumed elemental composition and hadronic interaction model. Here we show that an energy estimator independent of the primary mass composition can be defined by means of shower parameters measured in the core region, as carried out in the ARGO-YBJ experiment. The energy resolution is <10% above 100 TeV and gets better with energy increasing. Being insensitive to the number of muons, this energy determination has only a weak dependence on the hadronic interaction model. The features of this energy estimator have been validated by extensive MC simulations and used in the analysis of the ARGO-YBJ data

Contrast-enhanced ultrasound tracking of helical propellers with acoustic phase analysis and comparison with color Doppler

Medical microrobots (MRs) hold the potential to radically transform several interventional procedures. However, to guarantee therapy success when operating in hard-to-reach body districts, a precise and robust imaging strategy is required for monitoring and controlling MRs in real-time. Ultrasound (US) may represent a powerful technology, but MRs' visibility with US needs to be improved, especially when targeting echogenic tissues. In this context, motions of MRs have been exploited to enhance their contrast, e.g., by Doppler imaging. To exploit a more selective contrast-enhancement mechanism, in this study, we analyze in detail the characteristic motions of one of the most widely adopted MR concepts, i.e., the helical propeller, with a particular focus on its interactions with the backscattered US waves. We combine a kinematic analysis of the propeller 3D motion with an US acoustic phase analysis (APA) performed on the raw radio frequency US data in order to improve imaging and tracking in bio-mimicking environments. We validated our US-APA approach in diverse scenarios, aimed at simulating realistic in vivo conditions, and compared the results to those obtained with standard US Doppler. Overall, our technique provided a precise and stable feedback to visualize and track helical propellers in echogenic tissues (chicken breast), tissue-mimicking phantoms with bifurcated lumina, and in the presence of different motion disturbances (e.g., physiological flows and tissue motions), where standard Doppler showed poor performance. Furthermore, the proposed US-APA technique allowed for real-time estimation of MR velocity, where standard Doppler failed

Neoadjuvant therapy for breast cancer

Objective: To evaluate the frequency of neoadjuvant therapy (NT) in women with stage I–III breast cancer in Italy and whether it is influenced by biological characteristics, screening history, and geographic area. Methods: Data from the High Resolution Study conducted in 7 Italian cancer registries were used; they are a representative sample of incident cancers in the study period (2009–2013). Included were 3546 women aged &lt;85 years (groups &lt;50, 50–69, 70–64, and 75+) with stage I–III breast cancer at diagnosis who underwent surgery. Women were classified as receiving NT if they received chemotherapy, target therapy, and/or hormone therapy before the first surgical treatment. Logistic models were built to test the association with biological and contextual variables. Results: Only 8.2% of women (290 cases) underwent NT; the treatment decreases with increasing age (14.5% in age &lt;50 and 2.2% in age 75+), is more frequent in women with negative receptors (14.8%), HER2-positive (15.7%), and triple-negative (15.6%). The multivariable analysis showed the probability of receiving NT is higher in stage III (odds ratio [OR] 3.83; 95% confidence interval [CI] 2.83–5.18), luminal B (OR 1.87; 95% CI 1.27–2.76), triple-negatives (OR 1.88; 95% CI 1.15–3.08), and in symptomatic cancers (OR 1.98; 95% CI 1.13–3.48). Use of NT varied among geographic areas: Reggio Emilia had the highest rates (OR 2.29; 95% CI 1.37–3.82) while Palermo had the lowest (OR 0.41; 95% CI 0.24–0.68). Conclusions: The use of NT in Italy is limited and variable. There are no signs of greater use in hospitals with more advanced care

Designing and interpreting 'multi-omic' experiments that may change our understanding of biology.

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment

Photoacoustic Monitoring Of Magnetite-crystal Formation From Iron(iii) Hydroxide Acetate: Comparison With Esr Results

The formation of crystalline magnetite by 1-h heat treatment of iron(III) hydroxide acetate is described. This amorphous-crystalline solid transformation is monitored by electron-spin resonance and photoacoustic spectroscopies. No significant changes were detected for samples heated below 190 °C. Above this temperature both techniques presented results following a definite pattern, namely, the enhancement of ion mobility leading to particle growth and crystallization for a temperature up to 240 °C and the onset of magnetic ordering of magnetite near this temperature.65125150515

Extracting information from multiplex networks

11 pages; 5 figure