Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations

Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

Phase diagram and superconductivity of calcium borohyrides at extreme pressures

Motivated by the recent discovery of near-room temperature superconductivity in high-pressure superhydrides, we investigate from first principles the high-pressure superconducting phase diagram of the ternary Ca-B-H system, using ab initio evolutionary crystal structure prediction, and Density Functional Perturbation Theory. We find that below 100 GPa all stable and weakly metastable phases are insulating. This pressure marks the appearance of several new chemically-forbidden phases on the hull of stability, and the first onset of metalization in CaBH5. Metallization is then gradually achieved at higher pressure at different compositions. Among the metallic phases stable in the Megabar regime, we predict two high-Tc superconducting phases with CaBH6 and Ca2B2H13 compositions, with critical temperatures of 119 and 89 K at 300 GPa, respectively, surviving to lower pressures. Ternary hydrides will most likely play a major role in superconductivity research in the coming years; our study suggests that, in order to reduce the pressure for the onset of metallicity and superconductivity, further explorations of ternary hydrides should focus on elements less electronegative than boron

Worldwide occurrence of haemoplasmas in wildlife: Insights into the patterns of infection, transmission, pathology and zoonotic potential

Haemotropic mycoplasmas (haemoplasmas) have increasingly attracted the attention of wildlife disease researchers due to a combination of wide host range, high prevalence and genetic diversity. A systematic review identified 75 articles that investigated haemoplasma infection in wildlife by molecular methods (chiefly targeting partial 16S rRNA gene sequences), which included 131 host genera across six orders. Studies were less common in the Eastern Hemisphere (especially Africa and Asia) and more frequent in the Artiodactyla and Carnivora. Meta-analysis showed that infection prevalence did not vary by geographic region nor host order, but wild hosts showed significantly higher prevalence than captive hosts. Using a taxonomically flexible machine learning algorithm, we also found vampire bats and cervids to have greater prevalence, whereas mink, a subclade of vesper bats, and true foxes all had lower prevalence compared to the remaining sampled mammal phylogeny. Haemoplasma genotype and nucleotide diversity varied little among wild mammals but were marginally lower in primates and bats. Coinfection with more than one haemoplasma species or genotype was always confirmed when assessed. Risk factors of infection identified were sociality, age, males and high trophic levels, and both prevalence and diversity were often higher in undisturbed environments. Haemoplasmas likely use different and concurrent transmission routes and typically display enzootic dynamics when wild populations are studied longitudinally. Haemoplasma pathology is poorly known in wildlife but appears subclinical. Candidatus Mycoplasma haematohominis, which causes disease in humans, probably has it natural host in bats. Haemoplasmas can serve as a model system in ecological and evolutionary studies, and future research on these pathogens in wildlife must focus on increasing the geographic range and taxa of studies and elucidating pathology, transmission and zoonotic potential. To facilitate such work, we recommend using universal PCR primers or NGS protocols to detect novel haemoplasmas and other genetic markers to differentiate among species and infer cross-species transmission

A Multi-modal Brain Image Registration Framework for US-guided Neuronavigation Systems - Integrating MR and US for Minimally Invasive Neuroimaging

US-guided neuronavigation exploits the simplicity of use and minimal invasiveness of Ultrasound (US) imaging and the high tissue resolution and signal-to-noise ratio of Magnetic Resonance Imaging (MRI) to guide brain surgeries. More specifically, the intra-operative 3D US images are combined with pre-operative MR images to accurately localise the course of instruments in the operative field with minimal invasiveness. Multi-modal image registration of 3D US and MR images is an essential part of such system. In this paper, we present a complete software framework that enables the registration US and MR brain scans based on a multi resolution deformable transform, tackling elastic deformations (i.e. brain shifts) possibly occurring during the surgical procedure. The framework supports also simpler and faster registration techniques, based on rigid or affine transforms, and enables the interactive visualisation and rendering of the overlaid US and MRI volumes. The registration was experimentally validated on a public dataset of realistic brain phantom images, at different levels of artificially induced deformations

W2WNet: A two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality

Ideally, Convolutional Neural Networks (CNNs) should be trained with high quality images with minimum noise and correct ground truth labels. Nonetheless, in many real-world scenarios, such high quality is very hard to obtain, and datasets may be affected by any sort of image degradation and mislabelling issues. This negatively impacts the performance of standard CNNs, both during the training and the inference phase. To address this issue we propose Wise2WipedNet (W2WNet), a new two-module Convolutional Neural Network, where a Wise module exploits Bayesian inference to identify and discard spurious images during the training and a Wiped module takes care of the final classification, while broadcasting information on the prediction confidence at inference time. The goodness of our solution is demonstrated on a number of public benchmarks addressing different image classification tasks, as well as on a real-world case study on histological image analysis. Overall, our experiments demonstrate that W2WNet is able to identify image degradation and mislabelling issues both at training and at inference time, with positive impact on the final classification accuracy

VHMPID: a new detector for the ALICE experiment at LHC

This article presents the basic idea of VHMPID, an upgrade detector for the ALICE experiment at LHC, CERN. The main goal of this detector is to extend the particle identification capabilities of ALICE to give more insight into the evolution of the hot and dense matter created in Pb-Pb collisions. Starting from the physics motivations and working principles the challenges and current status of development is detailed.Comment: 4 pages, 6 figures. To be published in EPJ Web of Conference

INFN What Next: Ultra-relativistic Heavy-Ion Collisions

This document was prepared by the community that is active in Italy, within INFN (Istituto Nazionale di Fisica Nucleare), in the field of ultra-relativistic heavy-ion collisions. The experimental study of the phase diagram of strongly-interacting matter and of the Quark-Gluon Plasma (QGP) deconfined state will proceed, in the next 10-15 years, along two directions: the high-energy regime at RHIC and at the LHC, and the low-energy regime at FAIR, NICA, SPS and RHIC. The Italian community is strongly involved in the present and future programme of the ALICE experiment, the upgrade of which will open, in the 2020s, a new phase of high-precision characterisation of the QGP properties at the LHC. As a complement of this main activity, there is a growing interest in a possible future experiment at the SPS, which would target the search for the onset of deconfinement using dimuon measurements. On a longer timescale, the community looks with interest at the ongoing studies and discussions on a possible fixed-target programme using the LHC ion beams and on the Future Circular Collider.Comment: 99 pages, 56 figure

Notulae to the Italian flora of algae, bryophytes, fungi and lichens: 5

In this contribution, new data concerning bryophytes, fungi, and lichens of the Italian flora are presented. It includes new records and confirmations for the bryophyte genera Diplophyllum and Ptychostomum, the fungal genera Arrhenia, Gymnosporangium, and Sporidesmium and the lichen genera Arthonia, Coenogonium, Flavoplaca, Gyalolechia, Parmotrema, Peltigera, Pterygiopsis, Squamarina, Tornabea, and Waynea

Dark matter search in a Beam-Dump eXperiment (BDX) at Jefferson Lab

MeV-GeV dark matter (DM) is theoretically well motivated but remarkably unexplored. This Letter of Intent presents the MeV-GeV DM discovery potential for a 1 m$^3$ segmented plastic scintillator detector placed downstream of the beam-dump at one of the high intensity JLab experimental Halls, receiving up to 10$^{22}$ electrons-on-target (EOT) in a one-year period. This experiment (Beam-Dump eXperiment or BDX) is sensitive to DM-nucleon elastic scattering at the level of a thousand counts per year, with very low threshold recoil energies ($\sim$1 MeV), and limited only by reducible cosmogenic backgrounds. Sensitivity to DM-electron elastic scattering and/or inelastic DM would be below 10 counts per year after requiring all electromagnetic showers in the detector to exceed a few-hundred MeV, which dramatically reduces or altogether eliminates all backgrounds. Detailed Monte Carlo simulations are in progress to finalize the detector design and experimental set up. An existing 0.036 m$^3$ prototype based on the same technology will be used to validate simulations with background rate estimates, driving the necessary R$\&$D towards an optimized detector. The final detector design and experimental set up will be presented in a full proposal to be submitted to the next JLab PAC. A fully realized experiment would be sensitive to large regions of DM parameter space, exceeding the discovery potential of existing and planned experiments by two orders of magnitude in the MeV-GeV DM mass range.Comment: 28 pages, 17 figures, submitted to JLab PAC 4