S. Bonechi

The GAMMA-400 experiment: Status and prospects

The development of the GAMMA-400 γ-ray telescope continues. The GAMMA-400 is designed to measure fluxes of γ-rays and the electron-positron cosmic-ray component possibly associated with annihilation or decay of dark matter particles; and to search for and study in detail discrete γ-ray sources, to measure the energy spectra of Galactic and extragalactic diffuse γ-rays, and to study γ-ray bursts and γ-rays from the active Sun. The energy range for measuring γ-rays and electrons (positrons) is from 100 MeV to 3000 GeV. For 100-GeV γ-rays, the γ-ray telescope has an angular resolution of ∼0.01°, an energy resolution of ∼1%, and a proton rejection factor of ∼5 × 105. The GAMMA-400 will be installed onboard the Russian Space Observatory.

CALET UPPER LIMITS on X-RAY and GAMMA-RAY COUNTERPARTS of GW151226

We present upper limits in the hard X-ray and gamma-ray bands at the time of the LIGO gravitational-wave event GW 151226 derived from the CALorimetric Electron Telescope (CALET) observation. The main instrument of CALET, CALorimeter (CAL), observes gamma-rays from ~1 GeV up to 10 TeV with a field of view of ~2 sr. The CALET gamma-ray burst monitor (CGBM) views ~3 sr and ~2pi sr of the sky in the 7 keV - 1 MeV and the 40 keV - 20 MeV bands, respectively, by using two different scintillator-based instruments. The CGBM covered 32.5% and 49.1% of the GW 151226 sky localization probability in the 7 keV - 1 MeV and 40 keV - 20 MeV bands respectively. We place a 90% upper limit of 2 x 10^{-7} erg cm-2 s-1 in the 1 - 100 GeV band where CAL reaches 15% of the integrated LIGO probability (~1.1 sr). The CGBM 7 sigma upper limits are 1.0 x 10^{-6} erg cm-2 s-1 (7-500 keV) and 1.8 x 10^{-6} erg cm-2 s-1 (50-1000 keV) for one second exposure. Those upper limits correspond to the luminosity of 3-5 x 10^{49} erg s-1 which is significantly lower than typical short GRBs.

Automatic image classification for the urinoculture screening

Urinary tract infections (UTIs) are considered to be the most common bacterial infection and, actually, it is estimated that about 150 million UTIs occur world wide yearly, giving rise to roughly

The CALorimetric Electron Telescope (CALET) for high-energy astroparticle physics on the International Space Station

6 billion in healthcare expenditures and resulting in 100,000 hospitalizations. Nevertheless, it is difficult to carefully assess the incidence of UTIs, since an accurate diagnosis depends both on the presence of symptoms and on a positive urinoculture, whereas in most outpatient settings this diagnosis is made without an ad hoc analysis protocol. On the other hand, in the traditional urinoculture test, a sample of midstream urine is put onto a Petri dish, where a growth medium favors the proliferation of germ colonies. Then, the infection severity is evaluated by a visual inspection of a human expert, an error prone and lengthy process. In this paper, we propose a fully automated system for the urinoculture screening that can provide quick and easily traceable results for UTIs. Based on advanced image processing and machine learning tools, the infection type recognition, together with the estimation of the bacterial load, can be automatically carried out, yielding accurate diagnoses. The proposed AID (Automatic Infection Detector) system provides support during the whole analysis process: first, digital color images of Petri dishes are automatically captured, then specific preprocessing and spatial clustering algorithms are applied to isolate the colonies from the culture ground and, finally, an accurate classification of the infections and their severity evaluation are performed. The AID system speeds up the analysis, contributes to the standardization of the process, allows result repeatability, and reduces the costs. Moreover, the continuous transition between sterile and external environments (typical of the standard analysis procedure) is completely avoided.

Automatic Image Classification for the Urinoculture Screening

The CALorimetric Electron Telescope (CALET) is a space experiment, currently under development by Japan in collaboration with Italy and the United States, which will measure the flux of cosmic-ray electrons (and positrons) up to 20 TeV energy, of gamma rays up to 10 TeV, of nuclei with Z from 1 to 40 up to 1 PeV energy, and will detect gamma-ray bursts in the 7 keV to 20 MeV energy range during a 5 year mission. These measurements are essential to investigate possible nearby astrophysical sources of high energy electrons, study the details of galactic particle propagation and search for dark matter signatures. The main detector of CALET, the Calorimeter, consists of a module to identify the particle charge, followed by a thin imaging calorimeter (3 radiation lengths) with tungsten plates interleaving scintillating fibre planes, and a thick energy measuring calorimeter (27 radiation lengths) composed of lead tungstate logs. The Calorimeter has the depth, imaging capabilities and energy resolution necessary for excellent separation between hadrons, electrons and gamma rays. The instrument is currently being prepared for launch (expected in 2015) to the International Space Station ISS, for installation on the Japanese Experiment Module - Exposure Facility (JEM-EF).

CALOCUBE: an approach to high-granularity and homogenous calorimetry for space based detectors

Urinary Tract Infections (UTIs) represent a significant health problem, both in hospital and community–based settings. Normally, UTIs are diagnosed by traditional methods, based on cultivation of bacteria on Petri dishes, followed by a visual evaluation by human experts. In this paper, we present a fully automated system for the screening, that can provide quick and traceable results of UTIs. Actually, based on image processing techniques and machine learning tools, the recognition of bacteria and the colony count are automatically carried out, yielding accurate results. The proposed system, called AID (Automatic Infections Detector) provides support during the whole analysis process: first digital color images of the Petri dishes are automatically captured, then specific preprocessing and spatial clustering algorithms isolate the colonies from the culture ground, finally an accurate classification of the infection types and their severity is performed. Some important aspects of AID are: reduced time, results repeatability, reduced costs.

Extraction of High Level Visual Features for the Automatic Recognition of UTIs

Future space experiments dedicated to the observation of high-energy gamma and cosmic rays will increasingly rely on a highly performing calorimetry apparatus, and their physics performance will be primarily determined by the geometrical dimensions and the energy resolution of the calorimeter deployed. Thus it is extremely important to optimize its geometrical acceptance, the granularity, and its absorption depth for the measurement of the particle energy with respect to the total mass of the apparatus which is the most important constraint for a space launch. The proposed design tries to satisfy these criteria while staying within a total mass budget of about 1.6 tons. Calocube is a homogeneous calorimeter instrumented with Cesium iodide (CsI) crystals, whose geometry is cubic and isotropic, so as to detect particles

Automatic Image Analysis and Classification for Urinary Bacteria Infection Screening

Urinary Tract Infections (UTIs) are a severe public health problem, accounting for more than eight million visits to health care providers each year. High recurrence rates and increasing antimicrobial resistance among uropathogens threaten to greatly increase the economic burden of these infections. Normally, UTIs are diagnosed by traditional methods, based on cultivation of bacteria on Petri dishes, followed by a visual evaluation by human experts. The need of achieving faster and more accurate results, in order to set a targeted and sudden therapy, motivates the design of an automatic solution in place of the standard procedure. In this paper, we propose an algorithm that combines a “bag–of–words” approach with machine learning techniques to recognize infected plates and provide the automatic classification of the bacterial species. Preliminary experimental results are promising and motivate the introduction of a visual word dictionary with respect to using low level visual features.

CaloCube: A new-concept calorimeter for the detection of high-energy cosmic rays in space☆

In this paper, we present an automatic system for the screening of urinary tract infections. It is estimated that about 150 million infections of this kind occur world wide yearly, giving rise to roughly five billion health–care expenditures. Currently, Petri plates seeded with infected samples are analyzed by human experts, an error prone and lengthy process. Nevertheless, based on image processing techniques and machine learning tools, the recognition of the bacterium type and the colony count can be automatically carried out. The proposed system captures a digital image of the plate and, after a preprocessing stage to isolate the colonies from the culture ground, accurately identifies the infection type and severity. Moreover, it contributes to the standardization of the analysis process, also avoiding the continuous transition between sterile and external environments, which is typical in the classical laboratory procedure.

A Deep Learning Approach to Bacterial Colony Segmentation

Abstract The direct observation of high-energy cosmic rays, up to the PeV region, will increasingly rely on highly performing calorimeters, and the physics performance will be primarily determined by their geometrical acceptance and energy resolution. Thus, it is extremely important to optimize their geometrical design, granularity, and absorption depth, with respect to the total mass of the apparatus, which is among the most important constraints for a space mission. Calocube is a homogeneous calorimeter whose basic geometry is cubic and isotropic, so as to detect particles arriving from every direction in space, thus maximizing the acceptance; granularity is obtained by filling the cubic volume with small cubic scintillating crystals. This design forms the basis of a three-year R & D activity which has been approved and financed by INFN. A comparative study of different scintillating materials has been performed. Optimal values for the size of the crystals and spacing among them have been studied. Different geometries, besides the cubic one, and the possibility to implement dual-readout techniques have been investigated. A prototype, instrumented with CsI(Tl) cubic crystals, has been constructed and tested with particle beams. An overview of the obtained results will be presented and the perspectives for future space experiments will be discussed.