Search

In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on).In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-out validations have been used to assess the classification accuracy of the method. Also, the best classification models are tested on new data, showing excellent accuracy, even compared with existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.

Lawns provide green infrastructure and ecosystem services for anthropized areas. They have a strong impact on the environment in terms of inputs (water and fertilizers) and maintenance. The use of warm-season grasses, such as Cynodon dactylon (L.) Pers., provides a cost-effective and sustainable lawn in the dry summers of the Mediterranean. In winter, Bermudagrass is dormant and brown, which instead of being a problem could be an opportunity for biodiversity through the coexistence of flowering species. This study assesses the possibility of growing autumn-to-spring-flowering bulbs and forbs with Bermudagrass, to provide ecosystem services in urban areas. Eight geophytes and 18 forbs were incorporated into a mature turf of hybrid Bermudagrass, Cynodon dactylon × C. transvaalensis cv. “Tifway”. At the same time, a commercial flowering mix was sown in the same conditions. Two different soil preparations, scalping and turf flaming, and two different nitrogen doses, 50 and 150 kg ha−1, were carried out before sowing and transplanting. The flowering plants were counted. All the bulbs and six of the 18 forbs were able to grow and flower in the first and second years. The commercial mix was in full bloom from April until the cutting time for the hybrid Bermudagrass, at the end of May. Adding the flowering species did not affect the healthy growth of the warm-season grass. The fertilization dose had no effect, while turf flaming led to a wider spread of Bellis perennis L. and Crocus spp. Several flower-visiting insects were observed in the spring.

Green roofs are roof free spaces where living organisms can find an appropriate habitat to colonise. The establishment of plant species with different functionality can enhance biodiversity and provide ecosystem services. However, drought and nutrient availability can affect the plant development. The extensive green roof was set up in Pisa (Italy) in 2014, 12 modules of 10 cm depth were filled with three substrates composed of compost from municipal mixed waste, pelletised paper sludge, and commercial tephra product (Vulcaflor), as follows: Vulcaflor + compost, Vulcaflor + pellet + compost, and Vulcaflor + pellet, characterised by decreasing level of nitrogen content. The species planted in 2014 were chosen from the herbaceous spontaneous vegetation of urban and rural swards not often mowed, plus two sedum species. After the establishment phase, the green roof community was progressively dominated by Sedum species and other species were seeded in 2016. In 2018-19 the plant functional types and the community structure were monitored. Besides seasonal fluctuations, nitrogen shaped the composition of the community, and Sedum species showed high cover values in nitrogen-richer substrates. Annual forbs colonised the plots with a lower nitrogen content. In summer, the number of species drastically fell, and Sedum album was dominant in the three substrates. Seedling recruitment regenerated the community in the cooler season, increasing the diversity in the poor substrate. The scarcity of nitrogen led to the development of stress-tolerator annuals increasing the biodiversity in the rainy-cool season. Annual species constitute a transient seed bank which enables the system to regenerate when rain follows periods of heat and drought.

Radiomics is encouraging a paradigm shift in oncological diagnostics towards the symbiosis of radiology and Artificial Intelligence (AI) techniques. The aim is to exploit very accurate, robust image processing algorithms and provide quantitative information about the phenotypic differences of cancer traits. By exploring the association between this quantitative information and patients' prognosis, AI algorithms are boosting the power of radiomics in the perspective of precision oncology. However, the choice of the most suitable AI method can determine the success of a radiomic application. The current state-of-the art methods in radiomics aim at extracting statistical features from biomedical images and, then, process them with Machine Learning (ML) techniques. Many works have been reported in the literature presenting various combinations of radiomic features and ML methods. In this preliminary study, we aim to analyse the performance of a radiomic approach to predict prostate cancer (PCa) aggressiveness from multiarametric Magnetic Resonance Imaging (mp-MRI). Clinical mp-MRI data were collected from patients with histology-confirmed PCa and labelled by a team of expert radiologists. Such data were used to extract and select two sets of radiomic features; hence, the classification performances of five classifiers were assessed. This analysis is meant as a preliminary step towards the overall goal of investigating the potential of radiomic-based analyses.

Radiomics can quantify tumor phenotypic characteristics non-invasively by defining a signature correlated with biological information. Thanks to algorithms derived from computer vision to extract features from images, and machine learning methods to mine data, Radiomics is the perfect case study of application of Artificial Intelligence in the context of precision medicine. In this study we investigated the association between radiomic features extracted from multi-parametric magnetic resonance imaging (mp-MRI)of prostate cancer (PCa) and the tumor histologic subtypes (using Gleason Score) using machine learning algorithms, in order to identify which of the mp-MRI derived radiomic features can distinguish high and low risk PCa.

Urban cultivation for food production is of growing importance. The quality of urban soil can be improved by tillage and the incorporation of organic matter, or can be degraded by chemical treatments. Urban gardeners have a role in this process, through the selection of various cultivation techniques. Our study focuses on an allotment area in the town of Pisa (Italy), which since 1995 has been run as a municipal vegetable garden by the residents. We analysed the soil and compared the data with those collected five years previously, to verify the possible changes in soil properties and fertility. We also interviewed the gardeners regarding their backgrounds, motivations and cultivation practices. We looked for possible changes in the soil quality attributable to the cultivation techniques. We found that the allotment holders influenced the soil quality through the cultivation techniques. Organic carbon, electrical conductivity and the content of copper increased unevenly in relation to the gardeners' cultivation practices. At the same time the study highlights that the urban gardeners were not completely aware of how to protect and enhance the fertility and the quality of urban soil. We believe that town councils should be responsible for providing correct information to the allotment holders and thus prevent the possible misuse of urban soil to grow food, as this can affect everyone's health.

We present a parallelizzable, multilevel algorithm for the study of three-dimensional structure of biological macromolecules, applied to two fundamental topics: the 3D reconstruction of Chromatin and the elaboration of motion of proteins. For Chromatin, starting from contact data obtained through Chromosome Conformation Capture techniques, our method first subdivides the data matrix in biologically relevant blocks, and then treats them separately, at several levels, depending on the initial data resolution. The result is a family of configurations for the entire fiber, each one compatible with both experimental data and prior knowledge about specific genomes. For Proteins, the method is conceived as a solution for the problem of identifying motion and alternative conformations to the deposited structures. The algorithm, using quaternions, processes the main chain and the aminoacid side chains independently; it then exploits a Monte Carlo method for selection of biologically acceptable conformations, based on energy evaluation, and finally returns a family of conformations and of trajectories at single atom resolution.

Based on experimental techniques of the type Chromosome Conformation Capture (3C), several methods have been proposed in the literature to estimate the structure of the nuclear dna in homogeneous populations of cells. Many of these methods transform contact frequencies into Euclidean distances between pairs of chromatin fragments, and then reconstruct the structure by solving a distance-to-geometry problem. To avoid the drawbacks of this strategy, we propose to abandon the frequency-distance translation and adopt a recursive multiscale procedure, where the chromatin fibre is modelled by a new kind of modified bead chain, the data are suitably partitioned at each scale, and the resulting partial structures are estimated independently of each other and then connected again to rebuild the whole chain.We propose a new score function to generate the solution space: it includes a data-fit part that does not require target distances, and a penalty part, which enforces soft geometric constraints on the solution, coherent with known physical and biological constraints. The relative weights of the two parts are balanced automatically at each scale and each subchain treated. Since it is reasonable to expect that many different structures fit any 3c-type data set, we sample the solution space by simulated annealing, with no search for an absolute optimum. A set of different solutions with similar scores is thus generated. The procedure can be managed through a minimum set of parameters, independent of both the scale and the particular genomic segment being treated. The user is thus allowed to control the solutions easily and effectively. The partition of the fibre, along with several intrinsically parallel parts, make this method computationally efficient.We report some results obtained with the new method and code, tested against real data, that support the reliability of our method and the biological plausibility of our solutions.

The concept of urban sustainability and liveability closely depends on multi-level approaches to environmental issues. The ultimate goal in the field of noise management is to involve citizens and facilitate their participation in urban environmental decisions. The SENSEable Pisa project, based on the concept of Real-Time City and Smart City, presents an acoustic urban monitoring system based on a low-cost data acquisition method for a pervasive outdoor noise measurement. The system is based on the use of noise sensors located on private houses in the centre of Pisa, which provide a good model for the current acoustic climate of Mediterranean city centres. In this study, SENSEable acquisitions show a strong anthropogenic component not revealed by public strategic maps. The anthropogenic component, commonly known as movida, becomes increasingly critical in Mediterranean cities, therefore, it is necessary to explore methods highlighting this new source and to adopt strategies for the creation of reliable noise pollution maps.

Our goal in the framework of the Italian Flagship Project InterOmics is to reconstruct a set of plausible chromatin configurations from Chromosome Conformation Capture data. To this end, we rely on a simulated annealing algorithm that samples the solution space defined by a data-fit function and a multiscale chromatin model. The data-fit only accounts for the largest, most reliable contact frequencies, in order to avoid deriving distances inconsistent with the Euclidean geometry. At each scale, the chromatin model consists in a chain of partially penetrable beads whose properties (bead sizes, elasticity, curvature, etc.) can be constrained through biochemical and biological knowledge. During the annealing process, the model configuration is evolved through quaternions rather than the usual Euler matrices, as this offers a number of advantages in terms of composition of successive perturbations and automatic satisfaction of the constraints. The output of the annealing scheme is not unique due to the degrees of freedom left by the geometrical constraints. This allows us to obtain multiple configurations compatible with both the data and the prior knowledge. We are validating our method by applying it to real Hi-C data from the long arm of the human Chromosome 1. The mean-square Euclidean distances computed from our results as functions of the genomic distances support previous experimental results indicating that highly expressed genomic regions are less compact than poorly transcribed regions.