Alignment Free Dissimilarities for Nucleosome Classification

Epigenetic mechanisms such as nucleosome positioning, histone modifications and DNA methylation play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have shown a role of DNA sequences in recruitment of epigenetic regulators. For this reason, the use of more suitable similarities or dissimilarity between DNA sequences could help in the context of epigenetic studies. In particular, alignment-free dissimilarities have already been successfully applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles…

Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM

Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. We have tested STREAM on several synthetic and real datasets generated with different single-cell techno…

A one class KNN for signal identification: a biological case study

The paper describes an application of a one class KNN to identify different signal patterns embedded in a noise structured background. The problem becomes harder whenever only one pattern is well-represented in the signal; in such cases, one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a multi layer model (MLM) that provides preliminary signal segmentation in an interval feature space. The one class KNN has been tested on synthetic and real (Saccharomyces cerevisiae) microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.

A memetic approach to discrete tomography from noisy projections

Discrete tomography deals with the reconstruction of images from very few projections, which is, in the general case, an NP-hard problem. This paper describes a new memetic reconstruction algorithm. It generates a set of initial images by network flows, related to two of the input projections, and lets them evolve towards a possible solution, by using crossover and mutation. Switch and compactness operators improve the quality of the reconstructed images during each generation, while the selection of the best images addresses the evolution to an optimal result. One of the most important issues in discrete tomography is known as the stability problem and it is tackled here, in the case of no…

A Layered Architecture for Sentiment Classification of Products Reviews in Italian Language

The paper illustrates a system for the automatic classification of the sentiment orientation expressed into reviews written in Italian language. A proper stratification of linguistic resources is adopted in order to solve the lacking of an opinion lexicon specifically suited for the Italian language. Experiments show that the proposed system can be applied to a wide range of domains.

A MULTI-LAYER MODEL TO STUDY GENOME-SCALE POSITIONS OF NUCLEOSOMES

The positioning of nucleosomes along chromatin has been implicated in the regulation of gene expression in eukaryotic cells, because packaging DNA into nucleosomes affects sequence accessibility. In this paper we propose a new model (called MLM) for the identification of nucleosomes and linker regions across DNA, consisting in a thresholding technique based on cut-set conditions. For this purpose we have defined a method to generate synthetic microarray data fully inspired from the approach that has been used by Yuan et al. Results have shown a good recognition rate on synthetic data, moreover, the $MLM$ shows a good agreement with the recently published method based on Hidden Markov Model …

An Island Strategy for Memetic Discrete Tomography Reconstruction

In this paper we present a parallel island model memetic algorithm for binary discrete tomography reconstruction that uses only four projections without any further a priori information. The underlying combination strategy consists in separated populations of agents that evolve by means of different processes. Agents progress towards a possible solution by using genetic operators, switch and a particular compactness operator. A guided migration scheme is applied to select suitable migrants by considering both their own and their sub-population fitness. That is, from time to time, we allow some individuals to transfer to different subpopulations. The benefits of this paradigm were tested in …

Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology, and Framework

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers.

Attention-based Model for Evaluating the Complexity of Sentences in English Language

The automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep learning- based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in tw…

Associations between Notch-2, Akt-1 and HER2/neu expression in invasive human breast cancer: a tissue microarray immunophenotypic analysis on 98 patients.

<i>Objective:</i> We aimed to investigate the existence of associations between well-established and newly recognized biological and phenotypic features of breast cancer involved in tumor progression and prognosis. <i>Methods:</i> Ninety-eight cases of invasive breast cancer were assessed for the immunohistochemical expression of estrogen and progesterone receptors, Ki-67, HER2, Akt-1, and Notch-2, using the tissue microarray technique. Data regarding tumor histotype, histological grade, tumor size and lymph node status were collected for each patient and included in the analysis. <i>Results:</i> Several significant associations between histological and/o…

Real-time detection of BRAF V600E mutation from archival hairy cell leukemia FFPE tissue by nanopore sequencing

The MinION is a miniaturized high-throughput next generation sequencing platform of novel conception. The use of nucleic acids derived from formalin-fixed paraffin-embedded samples is highly desirable, but their adoption for molecular assays is hurdled by the high degree of fragmentation and by the chemical-induced mutations stemming from the fixation protocols. In order to investigate the suitability of MinION sequencing on formalin-fixed paraffin-embedded samples, the presence and frequency of BRAF c.1799T > A mutation was investigated in two archival tissue specimens of Hairy cell leukemia and Hairy cell leukemia Variant. Despite the poor quality of the starting DNA, BRAF mutation was su…

2-methoxyestradiol impacts on amino acids-mediated metabolic reprogramming in osteosarcoma cells by interaction with NMDA receptor

Deregulation of serine and glycine metabolism, have been identified to function as metabolic regulators in supporting tumor cell growth. The role of serine and glycine in regulation of cancer cell proliferation is complicated, dependent on concentrations of amino acids and tissue-specific. D-serine and glycine are coagonists of N-methyl-D-aspartate receptor subunit GRIN1. Importantly, NMDA receptors are widely expressed in cancer cells and play an important role in regulation of cell death, proliferation and metabolism of numerous malignancies. The aim of the present work was to associate the metabolism of glycine and D-serine with the anticancer activity of 2-methoxyestradiol. 2-methoxyest…

Distance Functions, Clustering Algorithms and Microarray Data Analysis

Distance functions are a fundamental ingredient of classification and clustering procedures, and this holds true also in the particular case of microarray data. In the general data mining and classification literature, functions such as Euclidean distance or Pearson correlation have gained their status of de facto standards thanks to a considerable amount of experimental validation. For microarray data, the issue of which distance function works best has been investigated, but no final conclusion has been reached. The aim of this extended abstract is to shed further light on that issue. Indeed, we present an experimental study, involving several distances, assessing (a) their intrinsic sepa…

Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora

In this paper, we tackle the problem of the irony and sarcasm detection for the Italian language to contribute to the enrichment of the sentiment analysis field. We analyze and compare five deep-learning systems. Results show the high suitability of such systems to face the problem by achieving 93% of F1-Score in the best case. Furthermore, we briefly analyze the model architectures in order to choose the best compromise between performances and complexity.

Green Tea Catechins Induce Inhibition of PTP1B Phosphatase in Breast Cancer Cells with Potent Anti-Cancer Properties: In Vitro Assay, Molecular Docking, and Dynamics Studies

The catechins derived from green tea possess antioxidant activity and may have a potentially anticancer effect. PTP1B is tyrosine phosphatase that is oxidative stress regulated and is involved with prooncogenic pathways leading to the formation of a.o. breast cancer. Here, we present the effect of selected green tea catechins on enzymatic activity of PTP1B phosphatase and viability of MCF-7 breast cancer cells. We showed also the computational analysis of the most effective catechin binding with a PTP1B molecule. We observed that epigallocatechin, epigallocatechin gallate, epicatechin, and epicatechin gallate may decrease enzymatic activity of PTP1B phosphatase and viability of MCF-7 cells.…

Extracellular Vesicle microRNAs Contribute to the Osteogenic Inhibition of Mesenchymal Stem Cells in Multiple Myeloma

Osteolytic bone disease is the major complication associated with the progression of multiple myeloma (MM). Recently, extracellular vesicles (EVs) have emerged as mediators of MM-associated bone disease by inhibiting the osteogenic differentiation of human mesenchymal stem cells (hMSCs). Here, we investigated a correlation between the EV-mediated osteogenic inhibition and MM vesicle content, focusing on miRNAs. By the use of a MicroRNA Card, we identified a pool of miRNAs, highly expressed in EVs, from MM cell line (MM1.S EVs), expression of which was confirmed in EVs from bone marrow (BM) plasma of patients affected by smoldering myeloma (SMM) and MM. Notably,we found that miR-129-5p, whic…

A Neural Network model for the Evaluation of Text Complexity in Italian Language: a Representation Point of View

Abstract The goal of a text simplification system (TS) is to create a new text suited to the characteristics of a reader, with the final goal of making it more understandable.The building of an Automatic Text Simplification System (ATS) cannot be separated from a correct evaluation of the text complexity. In fact the ATS must be capable of understanding if a text should be simplified for the target reader or not. In a previous work we have presented a model capable of classifying Italian sentences based on their complexity level. Our model is a Long Short Term Memory (LSTM) Neural Network capable of learning the features of easy-to-read and complex-to-read sentences autonomously from a anno…

Shape-Based Features for Cat Ganglion Retinal Cells Classification

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward’s hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

A Novel Approach for Supporting Italian Satire Detection Through Deep Learning

Satire is a way of criticizing people (or ideas) by ridiculing them on political, social, and morals topics often used to denounce political and societal problems, leveraging comedic devices such as parody exaggeration, incongruity, etc.etera. Detecting satire is one of the most challenging computational linguistics tasks, natural language processing, and social multimedia sentiment analysis. In particular, as satirical texts include figurative communication for expressing ideas/opinions concerning people, sentiment analysis systems may be negatively affected; therefore, satire should be adequately addressed to avoid such systems’ performance degradation. This paper tackles automatic satire…

Deep learning architectures for prediction of nucleosome positioning from sequences data

Abstract Background Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by com…

Sperm DNA fragmentation: An early and reliable marker of air pollution.

Environmental factors could have a key role in the continuous and remarkable decline of sperm quality observed in the last decades. This study compared the seminal parameters and sperm DFI in men living in areas with different levels of air pollution. Results demonstrate that both steel plants workers and patients living in a high polluted area show a mean percentage of sperm DNA fragmentation above 30%, highlighting a clear sperm damage. In this work, two different techniques were used to measure sperm DNA damage in patients’ groups, finding in both cases a high sperm DFI in patients living in polluted areas. We candidate sperm DNA fragmentation as a valuable early marker of the presence…

Identification of Key miRNAs in Regulation of PPI Networks

In this paper, we explore the interaction between miRNA and deregulated proteins in some pathologies. Assuming that miRNA can influence mRNA and consequently the proteins regulation, we explore this connection by using an interaction matrix derived from miRNA-target data and PPI network interactions. From this interaction matrix and the set of deregulated proteins, we search for the miRNA subset that influences the deregulated proteins with a minimum impact on the not deregulated ones. This regulation problem can be formulated as a complex optimization problem. In this paper, we have tried to solve it by using the Genetic Algorithm Heuristic. As the main result, we have found a set of miRNA…

Deep Metric Learning for Transparent Classification of Covid-19 X-Ray Images

This work proposes an interpretable classifier for automatic Covid-19 classification using chest X-ray images. It is based on a deep learning model, in particular, a triplet network, devoted to finding an effective image embedding. Such embedding is a non-linear projection of the images into a space of reduced dimension, where homogeneity and separation of the classes measured by a predefined metric are improved. A K-Nearest Neighbor classifier is the interpretable model used for the final classification. Results on public datasets show that the proposed methodology can reach comparable results with state of the art in terms of accuracy, with the advantage of providing interpretability to t…

Serum BLyS/BAFF predicts the outcome of acute hepatitis C virus infection.

Summary.  B-lymphocyte stimulator/B activating factor (BLyS/BAFF) is a tumour necrosis factor-family cytokine that plays a key role in generating and maintaining the mature B-cell pool. BLyS/BAFF expression by macrophages is stimulated by interferon-γ and interleukin-10, and its serum levels are increased in chronic hepatitis C (CHC). The aim of this study was to assess serum levels of BLyS/BAFF in patients with acute hepatitis C (AHC) and correlate them with disease outcome. We studied 28 patients with AHC (14 males, mean age 59.3 ± 15 years), followed for at least 7 months since onset, comparing them with 86 CHC patients and 25 healthy blood donors (HBD). BLyS/BAFF levels were assessed at…

A Framework for Opening Data and Creating Advanced Services in the Health and Social Fields

Open data is publicly available data that can be universally and readily accessed, used, and redistributed. Open data holds particular potential in the health and social sectors but, presently, health and social data are often published in a ‘closed’ format.There are different tools that allow to ‘open’ data, clean, structure and process them in order to elaborate them and build advanced services but, unfortunately, there is no single tool that can be used to perform all different tasks. We believe that the availability of Open Data in the health and social fields should be greatly increased and a way for creating new health and social services should be provided. In this paper, we present …

Artificial neural networks for fault tollerance of an air-pressure sensor network

A meteorological tsunami, commonly called Meteotsunami, is a tsunami-like wave originated by rapid changes in barometric pressure that involve the displacement of a body of water. This phenomenon is usually present in the sea cost area of Mazara del Vallo (Sicily, Italy), in particular in the internal part of the seaport canal, sometimes making local population at risk. The Institute for Coastal Marine Environment (IAMC) of the National Research Council in Italy (CNR) have already conducted several studies upon meteotsunami phenomenon. One of the project has regarded the creation of a sensors network composed by micro-barometric sensors, located in 4 different stations close to the seaport …

Discrete Tomography Reconstruction Through a New Memetic Algorithm

Discrete tomography is a particular case of computerized tomography that deals with the reconstruction of objects made of just one homogeneous material, where it is sometimes possible to reduce the number of projections to no more than four. Most methods for standard computerized tomography cannot be applied in the former case and ad hoc techniques must be developed to handle so few projections.

Design, development and validation of a system for automatic help to medical text understanding

Abstract Objective The paper presents a web-based application, SIMPLE, that facilitates medical text comprehension by identifying the health-related terms of a medical text and providing the corresponding consumer terms and explanations. Background The comprehension of a medical text is often a difficult task for laypeople because it requires semantic abilities that can differ from a person to another, depending on his/her health-literacy level. Some systems have been developed for facilitating the comprehension of medical texts through text simplification, either syntactical or lexical. The ones dealing with lexical simplification usually replace the original text and do not provide additi…

Molecular mimicry in the post-COVID-19 signs and symptoms of neurovegetative disorders?

Many individuals who have severe forms of COVID-19 experience a suite of neurovegetative signs and symptoms (eg, tachycardia) after their recovery, suggesting that the imbalance of the sympathetic-parasympathetic activity of the autonomic nervous system1 could continue for many weeks or months after respiratory symptoms stop. Moreover, a reduction of the parasympathetic tone could have a role in restricting the cholinergic anti-inflammatory pathway, thus favouring hyperinflammation and cytokine storm in the most severe phases of the disease. As reported by Guglielmo Lucchese in The Lancet Microbe,2 SARS-CoV-2 can damage the nervous system via an indirect mechanism, resulting in a high preva…

Experiments on Concurrent Artificial Environment

We show how the simulation of concurrent system is of interest for both behavioral studies and strategies of learning applied on prey-predator problems. In our case learning studies into unknown environment have been applied to mobile units by using genetic algorithms (GA). A set of trajectories, generated by GA, are able to build a description of the external scene driving a predators to a prey. Here, an example of prey-predator strategy,based on field of forces, is proposed. The evolution of the corespondent system can be formalized as an optimization problem and, for that purpose, GA can be use to give the right solution at this problem. This approach could be applied to the autonomous r…

A multi-layer method to study genome-scale positions of nucleosomes

AbstractThe basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 bp of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and…

Combining one class fuzzy KNN’s

This paper introduces a parallel combination of N > 2 one class fuzzy KNN (FKNN) classifiers. The classifier combination consists of a new optimization procedure based on a genetic algorithm applied to FKNN’s, that differ in the kind of similarity used. We tested the integration techniques in the case of N = 5 similarities that have been recently introduced to face with categorical data sets. The assessment of the method has been carried out on two public data set, the Masquerading User Data (www.schonlau.net) and the badges database on the UCI Machine Learning Repository (http://www.ics.uci.edu/~mlearn/). Preliminary results show the better performance obtained by the fuzzy integration …

Augmented Reality Gamification for Human Anatomy

This paper focuses on the use of Augmented Reality technologies in relation to the introduction of game design elements to support university medical students in their learning activities during a human anatomy laboratory. In particular, the solution we propose will provide educational contents visually connected to the physical organ, giving also the opportunity to handle a 3D physical model that is a perfect reproduction of a real human organ.

Towards A Deep-Learning-Based Methodology for Supporting Satire Detection (S)

STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data

AbstractSingle-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.

Brief communication: Vehicle routing problem and UAV application in the post-earthquake scenario

Abstract. In this paper we simulate a Unmanned Aerial Vehicle's (UAV) recognition after a possible case of diffuse damage after a seismic event in the town of Acireale (Sicily, Italy). Given a set of sites (84 relevant buildings) and the range of the UAV, we are able to find the number of vehicles to employ and the shortest survey path. The problem of finding the shortest survey path is an operational research problem called Vehicle Routing Problem (VRP) whose solution is known to be computationally time-consuming. We used the Simulated Annealing (SA) heuristic that is able to provide stable solutions in relatively short computing time. We also examined the distribution of the cost of the s…

Pattern Classification from Multi-beam Acoustic Data Acquired in Kongsfjorden

Climate change is causing a structural change in Arctic ecosystems, decreasing the effectiveness that the polar regions have in cooling water masses, with inevitable repercussions on the climate and with an impact on marine biodiversity. The Svalbard islands under study are an area greatly influenced by Atlantic waters. This area is undergoing changes that are modifying the composition and distribution of the species present. The aim of this work is to provide a method for the classification of acoustic patterns acquired in the Kongsfjorden, Svalbard, Arctic Circle using multibeam technology. Therefore the general objective is the implementation of a methodology useful for identifying the a…

Multi-class Text Complexity Evaluation via Deep Neural Networks

Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results sho…

PTP1B phosphatase as a novel target of oleuropein activity in MCF-7 breast cancer model.

Phosphatase PTP1B has become a therapeutic target for the treatment of type 2-diabetes, whereas recent studies have revealed that PTP1B plays a pivotal role in pathophysiology and development of breast cancer. Oleuropein is a natural, phenolic compound with anticancer activity. The aim of this study was to address the question whether PTP1B constitutes a target for oleuropein in breast cancer MCF-7 cells. The cellular MCF-7 breast cancer model was used in the study. The experiments were performed using cellular viability tests, Elisa assays, immunoprecipitation, flow cytometry analyses and computer modelling. Herein, we evidenced that the reduced activity of phosphatase PTP1B after treatmen…

A one class classifier for Signal identification: a biological case study

The paper describes an application of a one-class KNN to identify different signal patterns embedded in a noise structured background. The problem become harder whenever only one pattern is well represented in the signal, in such cases one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM) that provides a preliminary signal segmentation in an interval feature space. The one-class KNN has been tested on synthetic data that simulate microarray data for the identification of nucleosomes and linker regions across DNA. Results have shown a good recognition rate on synthetic data for nucleosome and lin…

BITS2019: the sixteenth annual meeting of the Italian society of bioinformatics.

AbstractThe 16th Annual Meeting of the Bioinformatics Italian Society was held in Palermo, Italy, on June 26-28, 2019. More than 80 scientific contributions were presented, including 4 keynote lectures, 31 oral communications and 49 posters. Also, three workshops were organised before and during the meeting. Full papers from some of the works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

An integrated fuzzy cells-classifier

This paper introduces a genetic algorithm able to combine different classifiers based on different distance functions. The use of a genetic algorithm is motivated by the fact that the combination phase is based on the optimization of a vote strategy. The method has been applied to the classification of four types of biological cells, results show an improvement of the recognition rate using the genetic algorithm combination strategy compared with the recognition rate of each single classifier.

Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea)

This work presents a computational methodology able to automatically classify the echoes of two krill species recorded in the Ross sea employing scientific echo-sounder at three different frequencies (38, 120 and 200 kHz). The goal of classifying the gregarious species represents a time-consuming task and is accomplished by using differences and/or thresholds estimated on the energy features of the insonified targets. Conversely, our methodology takes into account energy, morphological and depth features of echo data, acquired at different frequencies. Internal validation indices of clustering were used to verify the ability of the clustering in recognizing the correct number of species. Th…

Muscle Histopathological Abnormalities in a Patient With a CCT5 Mutation Predicted to Affect the Apical Domain of the Chaperonin Subunit.

Recognition of diseases associated with mutations of the chaperone system genes, e.g., chaperonopathies, is on the rise. Hereditary and clinical aspects are established, but the impact of the mutation on the chaperone molecule and the mechanisms underpinning the tissue abnormalities are not. Here, histological features of skeletal muscle from a patient with a severe, early onset, distal motor neuropathy, carrying a mutation on the CCT5 subunit (MUT) were examined in comparison with normal muscle (CTR). The MUT muscle was considerably modified; atrophy of fibers and disruption of the tissue architecture were prominent, with many fibers in apoptosis. CCT5 was diversely present in the sarcolem…

A New Dissimilarity Measure for Clustering Seismic Signals

Hypocenter and focal mechanism of an earthquake can be determined by the analysis of signals, named waveforms, related to the wave field produced and recorded by a seismic network. Assuming that waveform similarity implies the similarity of focal parameters, the analysis of those signals characterized by very similar shapes can be used to give important details about the physical phenomena which have generated an earthquake. Recent works have shown the effectiveness of cross-correlation and/or cross-spectral dissimilarities to identify clusters of seismic events. In this work we propose a new dissimilarity measure between seismic signals whose reliability has been tested on real seismic dat…

Structural and Dynamic Disturbances Revealed by Molecular Dynamics Simulations Predict the Impact on Function of CCT5 Chaperonin Mutations Associated with Rare Severe Distal Neuropathies

Mutations in genes encoding molecular chaperones, for instance the genes encoding the subunits of the chaperonin CCT (chaperonin containing TCP-1, also known as TRiC), are associated with rare neurodegenerative disorders. Using a classical molecular dynamics approach, we investigated the occurrence of conformational changes and differences in physicochemical properties of the CCT5 mutations His147Arg and Leu224Val associated with a sensory and a motor distal neuropathy, respectively. The apical domain of both variants was substantially but differently affected by the mutations, although these were in other domains. The distribution of hydrogen bonds and electrostatic potentials on the surfa…

The Three Steps of Clustering In The Post-Genomic Era

This chapter descibes the basic algorithmic components that are involved in clustering, with particular attention to classification of microarray data.

Deep learning network for exploiting positional information in nucleosome related sequences

A nucleosome is a DNA-histone complex, wrapping about 150 pairs of double-stranded DNA. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells to form the Chromatin. Nucleosome positioning genome wide play an important role in the regulation of cell type-specific gene activities. Several biological studies have shown sequence specificity of nucleosome presence, clearly underlined by the organization of precise nucleotides substrings. Taking into consideration such advances, the identification of nucleosomes on a genomic scale has been successfully performed by DNA sequence features representation and classical supervised classification methods such as Support Vec…

A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis

Abstract Background Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function; (2) choice of a clustering algorithm; (3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Results A procedure is proposed for the assessment of the discriminative ability of a distance functi…

A Memetic Island Model for Discrete Tomography Reconstruction

Soft computing is a term indicating a coalition of methodologies, and its basic dogma is that, in general, better results can be obtained through the use of constituent methodologies in combination, rather than in a stand alone mode. Evolutionary computing belongs to this coalition, and thus memetic algorithms. Here, we present a combination of several instances of a recently proposed memetic algorithm for discrete tomography reconstruction, based on the island model parallel implementation. The combination is motivated by the fact that, even though the results of the recently proposed approach are finally better and more robust compared to other approaches, we advised that its major drawba…

A motif-independent metric for DNA sequence specificity

Abstract Background Genome-wide mapping of protein-DNA interactions has been widely used to investigate biological functions of the genome. An important question is to what extent such interactions are regulated at the DNA sequence level. However, current investigation is hampered by the lack of computational methods for systematic evaluating sequence specificity. Results We present a simple, unbiased quantitative measure for DNA sequence specificity called the Motif Independent Measure (MIM). By analyzing both simulated and real experimental data, we found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs. We…

Entropy measures in Image Classification

A Memetic Algorithm for Binary Image Reconstruction

This paper deals with a memetic algorithm for the reconstruction of binary images, by using their projections along four directions. The algorithm generates by network flows a set of initial images according to two of the input projections and lets them evolve toward a solution that can be optimal or close to the optimum. Switch and compactness operators improve the quality of the reconstructed images which belong to a given generation, while the selection of the best image addresses the evolution to an optimal output.

Interval Length Analysis in Multi Layer Model

In this paper we present an hypothesis test of randomness based on the probability density function of the symmetrized Kulback-Leibler distance estimated, via a Monte Carlo simulation, by the distributions of the interval lengths detected using the Multi-Layer Model (MLM). The $MLM$ is based on the generation of several sub-samples of an input signal; in particular a set of optimal cut-set thresholds are applied to the data to detect signal properties. In this sense MLM is a general pattern detection method and it can be considered a preprocessing tool for pattern discovery. At the present the test has been evaluated on simulated signals which respect a particular tiled microarray approach …

Machine Learning Models for Measuring Syntax Complexity of English Text

In this paper we propose a methodology to assess the syntax complexity of a sentence representing it as sequence of parts-of-speech and comparing Recurrent Neural Networks and Support Vector Machine. We have carried out experiments in English language which are compared with previous results obtained for the Italian one.

An Integrated fuzzy Cells-classifier

The term soft-computing has been introduced by Zadeh in 1994. Soft-computing provides an appropriate paradigm to program malleable and smooth concepts. In this paper a genetic algorithm is proposed to fuse the classification results due to different distance functions. The combination is based on the optimization of a vote strategy and it is applied to cells classification.

A New Feature Selection Methodology for K-mers Representation of DNA Sequences

DNA sequence decomposition into k-mers and their frequency counting, defines a mapping of a sequence into a numerical space by a numerical feature vector of fixed length. This simple process allows to compare sequences in an alignment free way, using common similarities and distance functions on the numerical codomain of the mapping. The most common used decomposition uses all the substrings of a fixed length k making the codomain of exponential dimension. This obviously can affect the time complexity of the similarity computation, and in general of the machine learning algorithm used for the purpose of sequence analysis. Moreover, the presence of possible noisy features can also affect the…

Bayesian versus data driven model selection for microarray data

Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is a particular instance of the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In what follows, for ease of reference, we refer to that instance still as model selection. It is an important part of any statistical analysis. The techniques used for solving it are mainly either Bayesian or data-driven, and are both based on internal knowledge. That is, they use information obtained by processing the input data. A…

A Lexicon-based Approach for Sentiment Classification of Amazon Books Reviews in Italian Language

We present a system aimed at the automatic classification of the sentiment orientation expressed into book reviews written in Italian language. The system we have developed is found on a lexicon-based approach and uses NLP techniques in order to take into account the linguistic relation between terms in the analyzed texts. The classification of a review is based on the average sentiment strenght of its sentences, while the classification of each sentence is obtained through a parsing process inspecting, for each term, a window of previous items to detect particular combinations of elements giving inversions or variations of polarity. The score of a single word depends on all the associated …

2-Methoxyestradiol Affects Mitochondrial Biogenesis Pathway and Succinate Dehydrogenase Complex Flavoprotein Subunit A in Osteosarcoma Cancer Cells.

Background/aim Dysregulation of mitochondrial pathways is implicated in several diseases, including cancer. Notably, mitochondrial respiration and mitochondrial biogenesis are favored in some invasive cancer cells, such as osteosarcoma. Hence, the aim of the current work was to investigate the effects of 2-methoxyestradiol (2-ME), a potent anticancer agent, on the mitochondrial biogenesis of osteosarcoma cells. Materials and methods Highly metastatic osteosarcoma 143B cells were treated with 2-ME separately or in combination with L-lactate, or with the solvent (non-treated control cells). Protein levels of α-syntrophin and peroxisome proliferator-activated receptor gamma, coactivator 1 alph…

A Deep Learning Model for Epigenomic Studies

Epigenetics is the study of heritable changes in gene expression that does not involve changes to the underlying DNA sequence, i.e. a change in phenotype not involved by a change in genotype. At least three main factor seems responsible for epigenetic change including DNA methylation, histone modification and non-coding RNA, each one sharing having the same property to affect the dynamic of the chromatin structure by acting on Nucleosomes posi- tion. A nucleosome is a DNA-histone complex, where around 150 base pairs of double-stranded DNA is wrapped. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells, to form the Chromatin. Nucleosome positioning plays an imp…

Induction of 2-hydroxycatecholestrogens O-methylation: A missing puzzle piece in diagnostics and treatment of lung cancer

Lung cancer is one of the most common cancers worldwide, causing nearly one million deaths each year. Herein, we present the effect of 2-methoxyestradiol (2-ME), the endogenous metabolite of 17β-estradiol (E2), on non-small cell lung cancer (NSCLC) cells. We observed that 2-ME reduced the viability of lung adenocarcinoma in two-dimensional (2D) and three-dimensional (3D) spheroidal A549 cell culture models. Molecular modeling was carried out aiming to visualize amino acid residues within binding pockets of the acyl-protein thioesterases, namely 1 (APT1) and 2 (APT2), and thus to identify which ones were more likely involved in the interaction with 2-ME. Our findings suggest that 2-ME acts a…

Human molecular chaperones share with SARS-CoV-2 antigenic epitopes potentially capable of eliciting autoimmunity against endothelial cells: possible role of molecular mimicry in COVID-19

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2), the cause of COVID-19 disease, has the potential to elicit autoimmunity because mimicry of human molecular chaperones by viral proteins. We compared viral proteins with human molecular chaperones, many of which are heat shock proteins, to determine if they share amino acid-sequence segments with immunogenic-antigenic potential, which can elicit cross-reactive antibodies and effector immune cells with the capacity to damage-destroy human cells by a mechanism of autoimmunity. We identified the chaperones that can putatively participate in molecular mimicry phenomena after SARS-CoV-2 infection, focusing on those for which endotheli…

A pattern recognition approach to identify biological clusters acquired by acoustic multi-beam in Kongsfjorden

The Svalbardsis one of the most intensively studied marine regions in the Artic; here the composition and distribution of marine assemblages are changing under the effect of global change, and marine communities are monitored in order to understand the long-term effects on marine biodiversity. In the present work, acoustic data collected in the Kongsfjorden using multi-beam technology was analyzed to develop a methodology for identifying and classifying 3D acoustic patterns related to fish aggregations. In particular, morphological, energetic and depth features were taken into account to develop a multi-variate classification procedure allowing to discriminate fish species. The results obta…

Recurrent Deep Neural Networks for Nucleosome Classification

Nucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid t…

An automatic system for helping health consumers to understand medical texts

Medical texts (reports, articles, etc.) are usually written by professionals (physicians, medical researchers, etc.) who use their own language and communication style. On the other hand, these texts are often read by health consumers (as in the case of medical reports) who do not have the same skills and vocabularies of the experts and can have difficulties in text comprehension. To help a health consumer in understanding a medical text, it would be desirable to have an automatic system that, given a text written with medical (technical) terms, translates them in simple or plain language and provides additional information with the same kind of language. We have designed such a system. It …

Erratum to: A New Feature Selection Methodology for K-mers Representation of DNA Sequences

Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection

Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses their own datasets to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine learning algorithms to input te…

Modification of DNA structure by reactive nitrogen species as a result of 2-methoxyestradiol–induced neuronal nitric oxide synthase uncoupling in metastatic osteosarcoma cells

Abstract 2-methoxyestradiol (2-ME) is a physiological anticancer compound, metabolite of 17β-estradiol. Previously, our group evidenced that from mechanistic point of view one of anticancer mechanisms of action of 2-ME is specific induction and nuclear hijacking of neuronal nitric oxide synthase (nNOS), resulting in local generation of nitro-oxidative stress and finally, cancer cell death. The current study aims to establish the substantial mechanism of generation of reactive nitrogen species by 2-ME. We further achieved to identify the specific reactive nitrogen species involved in DNA-damaging mechanism of 2-ME. The study was performed using metastatic osteosarcoma 143B cells. We detected…

A genetic integrated fuzzy classifier

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.

Applications of alignment-free methods in epigenomics

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic reg…

Development and Practical Use of a Medical Vocabulary-Thesaurus-Dictionary for Patient Empowerment

Health empowerment can be obtained through an informative and educational intervention to increase one's ability to think critically and act autonomously. Medical texts are usually written by professionals and can be difficulty understood by non experts who do not have the same skills and vocabularies. Thus, it would be desirable to have an online medical vocabulary-thesaurus-dictionary that can help a non expert to easily find the consumer equivalent of medical (technical) terms and additional consumer information. To this end, we have developed an online multilingual medical vocabulary-thesaurus-dictionary by interconnecting different online sources, i.e., medical vocabularies to create a…

Optimization of Low-Cost Monitoring Systems for On-Site Earthquake Early-Warning of Critical Infrastructures

In the last years, monitoring systems based on low-cost and miniaturized sensors (MEMS) revealed as a very successful compromise between the availability of data and their quality. Also applications in the field of seismic and structural monitoring have been constantly increasing in term of number and variety of functions. Among these applications, the implementation of systems for earthquake early warning is a cutting-edge topic, mainly for its relevance for the society as millions of peoples in various regions of the world are exposed to high seismic hazard. This paper introduces the optimization of an already established seismic (and structural) monitoring system, that would make it suit…

Deep learning models for bacteria taxonomic classification of metagenomic data.

Background An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions.…

A web search methodology for health consumers

Nowadays, many people use the World Wide Web to seek medical and health information but different users, such as providers (e.g., physicians) and consumers (e.g., patients), have different needs and bring different levels of reading ability and prior knowledge. Generic and specific search engines and specialized health sites either do not exploit the whole web or overload users with information. This creates difficulties mainly to consumers who often do not exactly know how to find the desired information. Thus, an information retrieval system for the web that 'drives' the user in finding the relevant information would be very beneficial. This paper describes a web search methodology for he…

Spread of tomato brown rugose fruit virus in sicily and evaluation of the spatiotemporal dispersion in experimental conditions

Tomato brown rugose fruit virus (ToBRFV) is an emerging pathogen that causes severe disease in tomato (Solanum lycopersicum L.) crops. The first ToBRFV outbreak in Italy occurred in 2018 in several Sicilian provinces, representing a serious threat for tomato production. In the present work, the spatiotemporal displacement of ToBRFV in Sicily was evaluated, analyzing a total of 590 lots of tomato seed, 982 lots of plantlets from nurseries and 100 commercial greenhouses. Furthermore, we investigated the ToBRFV spreading dynamic in a greenhouse under experimental conditions. Results showed several aspects related to ToBRFV dispersion in protected tomato crops. In detail, an important decrease …

Deep neural attention-based model for the evaluation of italian sentences complexity

In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.

A Fuzzy One Class Classifier for Multi Layer Model

The paper describes an application of a fuzzy one-class classifier (FOC ) for the identification of different signal patterns embedded in a noise structured background. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM ) that provides a preliminary signal segmentation in an interval feature space. The FOC has been tested on synthetic and real microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.

Variable Ranking Feature Selection for the Identification of Nucleosome Related Sequences

Several recent works have shown that K-mer sequence representation of a DNA sequence can be used for classification or identification of nucleosome positioning related sequences. This representation can be computationally expensive when k grows, making the complexity in spaces of exponential dimension. This issue effects significantly the classification task computed by a general machine learning algorithm used for the purpose of sequence classification. In this paper, we investigate the advantage offered by the so-called Variable Ranking Feature Selection method to select the most informative k − mers associated to a set of DNA sequences, for the final purpose of nucleosome/linker classifi…

Genome-wide characterization of chromatin binding and nucleosome spacing activity of the nucleosome remodelling ATPase ISWI

The evolutionarily conserved ATP-dependent nucleosome remodelling factor ISWI can space nucleosomes affecting a variety of nuclear processes. In Drosophila, loss of ISWI leads to global transcriptional defects and to dramatic alterations in higher-order chromatin structure, especially on the male X chromosome. In order to understand if chromatin condensation and gene expression defects, observed in ISWI mutants, are directly correlated with ISWI nucleosome spacing activity, we conducted a genome-wide survey of ISWI binding and nucleosome positioning in wild-type and ISWI mutant chromatin. Our analysis revealed that ISWI binds both genic and intergenic regions. Remarkably, we found that ISWI…

Deep Learning Architectures for DNA Sequence Classification

DNA sequence classification is a key task in a generic computational framework for biomedical data analysis, and in recent years several machine learning technique have been adopted to successful accomplish with this task. Anyway, the main difficulty behind the problem remains the feature selection process. Sequences do not have explicit features, and the commonly used representations introduce the main drawback of the high dimensionality. For sure, machine learning method devoted to supervised classification tasks are strongly dependent on the feature extraction step, and in order to build a good representation it is necessary to recognize and measure meaningful details of the items to cla…

GenClust: A genetic algorithm for clustering gene expression data

Abstract Background Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, …

CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

Abstract Background Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. Results In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel …

Image Segmentation based on Genetic Algorithms Combination

The paper describes a new image segmentation algorithm called Combined Genetic segmentation which is based on a genetic algorithm. Here, the segmentation is considered as a clustering of pixels and a similarity function based on spatial and intensity pixel features is used. The proposed methodology starts from the assumption that an image segmentation problem can be treated as a Global Optimization Problem. The results of the image segmentations algorithm has been compared with recent existing techniques. Several experiments, performed on real images, show good performances of our approach compared to other existing methods.

Bacteria classification using minimal absent words

Bacteria classification has been deeply investigated with different tools for many purposes, such as early diagnosis, metagenomics, phylogenetics. Classification methods based on ribosomal DNA sequences are considered a reference in this area. We present a new classificatier for bacteria species based on a dissimilarity measure of purely combinatorial nature. This measure is based on the notion of Minimal Absent Words, a combinatorial definition that recently found applications in bioinformatics. We can therefore incorporate this measure into a probabilistic neural network in order to classify bacteria species. Our approach is motivated by the fact that there is a vast literature on the com…

A Controllable Text Simplification System for the Italian Language

Text simplification is a non-trivial task that aims at reducing the linguistic complexity of written texts. Researchers have studied the problem by proposing new methodologies for addressing the English language, but other languages, like the Italian one, are almost unexplored. In this paper, we give a contribution to the enhancement of the Automated Text Simplification research by presenting a deep learning-based system, inspired by a state of the art system for the English language, capable of simplifying Italian texts. The system has been trained and tested by leveraging the Italian version of Newsela; it has shown promising results by achieving a SARI value of 30.17.

Dissimilarity Measures for the Identification of Earthquake Focal Mechanisms

This work presents a study about dissimilarity measures for seismic signals, and their relation to clustering in the particular problem of the identification of earthquake focal mechanisms, i.e. the physical phenomena which have generated an earthquake. Starting from the assumption that waveform similarity implies similarity in the focal parameters, important details about them can be determined by studying waveforms related to the wave field produced by earthquakes and recorded by a seismic network. Focal mechanisms identification is currently investigated by clustering of seismic events, using mainly cross-correlation dissimilarity in conjunction with hierarchical clustering algorithm. By…

A new Multi-Layers Method to Analyze Gene Expression

In the paper a new Multi-Layers approach (called Multi-Layers Model MLM) for the analysis of stochastic signals and its application to the analysis of gene expression data is presented. It consists in the generation of sub-samples from the input signal by applying a threshold technique based on cut-set optimal conditions. The MLM has been applied on synthetic and real microarray data for the identification of particular regions across DNA called nucleosomes and linkers. Nucleosomes are the fundamental repeating subunits of all eukaryotic chromatin, and their positioning provides useful information regarding the regulation of gene expression in eukaryotic cells. Results have shown a good rec…

Improving Communication in Risk Management of Health Information Technology Systems by means of Medical Text Simplification

Health Information Technology Systems (HITS) are increasingly used to improve the quality of patient care while reducing costs. These systems have been developed in response to the changing models of care to an ongoing relationship between patient and care team, supported by the use of technology due to the increased instance of chronic disease. However, the use of HITS may increase the risk to patient safety and security. While standards can be used to address and manage these risks, significant communication problems exist between experts working in different departments. These departments operate in silos often leading to communication breakdowns. For example, risk management stakeholder…

A Learned Sorted Table Search Library

This library includes a collection of methods for performing element search in ordered tables, starting from textbook implementations to more complex algorithms

A Benchmarking Platform for Atomic Learned Indexes

This repository provides a benchmarking platform to evaluate how Feed Forward Neural Networks can be effectively used as index data structures.