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

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

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

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

High-temperature conventional superconductivity in the boron-carbon system: material trends

In this paper, we probe the possibility of high-temperature conventional superconductivity in the boron-carbon system, using ab initio screening. A database of 320 metastable structures with fixed composition (50%/50%) is generated with the minima hopping method, and characterized with electronic and vibrational descriptors. Full electron-phonon calculations on 16 representative structures allow us to identify general trends in Tc across and within the four families in the energy landscape, and to construct an approximate Tc predictor, based on transparently interpretable and easily computable electronic and vibrational descriptors. Based on these, we estimate that around 10% of all metallic structures should exhibit Tc's above 30 K. This paper is a first step toward ab initio design of new high-Tc superconductors

A non-linear autoregressive model for indoor air-temperature predictions in smart buildings

In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40% of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short-and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models

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

Electrical impedance spectroscopy for real-time monitoring of the life cycle of graphene nanoplatelets filters for some organic industrial pollutants

This article proposes an approach for smart monitoring of the life cycle of innovative graphene-haled filters for water remediation in the presence of pollutants. The measurement technique is based on suitable figures of merit that analyze the time variation of the electrical impedance frequency spectrum. The proposed study considers the remediation of two toxic industrial pollutants, such as the acetonitrile and the 2,4-dichlorophenol. The contribution of this article is twofold. The first is the demonstration of a reliable monitoring setup that is able, for the selected use cases, to correlate in real time the behavior of the electrical impedance of the filter to its status, defined as "absence of pollutants" and/or "saturation." The second contribution is the proposal of suitable figures of merit, based on measurement of the impedance frequency spectrum, able to increase the measurement sensitivity and the reliability and to mitigate some sources of uncertainty typically associated with these kinds of setups and measurements. Results show that the proposed graphene-based filters combine very good filtering capability and high sensitivity of the electrical impedance to the considered pollutants. These results suggest further investigations with other pollutants and the potential use of this technique for the predictive maintenance of the water filters in industrial applications, by endowing the graphene filters of smart sensing devices