Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues.

In the last decades, early disease identification through non-invasive and automatic methodologies has gathered increasing interest from the scientific community. Among others, Parkinson's disease (PD) has received special attention in that it is a severe and progressive neuro-degenerative disease. As a consequence, early diagnosis would provide more effective and prompt care strategies, that cloud successfully influence patients' life expectancy.

A framework to identify structured behavioral patterns within rodent spatial trajectories.

Animal behavior is highly structured. Yet, structured behavioral patterns-or "statistical ethograms"-are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.

Social robots and therapeutic adherence: A new challenge in pediatric asthma?

Social Robots are used in different contexts and, in healthcare, they are better known as Socially Assistive Robots. In the context of asthma, the use of Socially Assistive Robots has the potential to increase motivation and engagement to treatment. Other positive roles proposed for Socially Assistive Robots are to provide education, training regarding treatments, and feedback to patients.

An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian.

Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better handle the Twitter jargon. This work aims to introduce a different approach for Twitter sentiment analysis based on two steps.

Knowledge-Based Verification of Concatenative Programming Patterns Inspired by Natural Language for Resource-Constrained Embedded Devices.

We propose a methodology to verify applications developed following programming patterns inspired by natural language that interact with physical environments and run on resource-constrained interconnected devices. Natural language patterns allow for the reduction of intermediate abstraction layers to map physical domain concepts into executable code avoiding the recourse to ontologies, which would need to be shared, kept up to date, and synchronized across a set of devices. Moreover, the computational paradigm we use for effective distributed execution of symbolic code on resource-constrained devices encourages the adoption of such patterns.

SARS-CoV-2 Infections and COVID-19 Fatality: Estimation of Infection Fatality Ratio and Current Prevalence.

COVID-19 is one of the most important problems for public health, according to the number of deaths associated to this pathology reported so far. However, from the epidemiological point of view, the dimension of the problem is still unknown, since the number of actual cases of SARS-CoV-2 infected people is underestimated, due to limited testing. This paper aims at estimating the actual Infection Fatality Ratio (number of deaths with respect to the number of infected people) and the actual current prevalence (number of infected people with respect to the entire population), both in a specific population and all over the world.

SAMMY-seq reveals early alteration of heterochromatin and deregulation of bivalent genes in Hutchinson-Gilford Progeria Syndrome.

Hutchinson-Gilford progeria syndrome is a genetic disease caused by an aberrant form of Lamin A resulting in chromatin structure disruption, in particular by interfering with lamina associated domains. Early molecular alterations involved in chromatin remodeling have not been identified thus far. Here, we present SAMMY-seq, a high-throughput sequencing-based method for genome-wide characterization of heterochromatin dynamics.

An hybrid ECG-based deep network for the early identification of high-risk to major cardiovascular events for hypertension patients.

Background And Objective: As the population becomes older and more overweight, the number of potential high-risk subjects with hypertension continues to increase. ICT technologies can provide valuable support for the early assessment of such cases since the practice of conducting medical examinations for the early recognition of high-risk subjects affected by hypertension is quite difficult, time-consuming, and expensive.

Methods: This paper presents a novel time series-based approach for the early identification of increases in hypertension to discriminate between cardiovascular high-risk and low-risk hypertensive patients through the analyses of electrocardiographic holter signals.

Network-based prediction of COVID-19 epidemic spreading in Italy.

Initially emerged in the Chinese city Wuhan and subsequently spread almost worldwide causing a pandemic, the SARS-CoV-2 virus follows reasonably well the Susceptible-Infectious-Recovered (SIR) epidemic model on contact networks in the Chinese case. In this paper, we investigate the prediction accuracy of the SIR model on networks also for Italy. Specifically, the Italian regions are a metapopulation represented by network nodes and the network links are the interactions between those regions.

Duodenal Metatranscriptomics to Define Human and Microbial Functional Alterations Associated with Severe Obesity: A Pilot Study.

Obesity is a multifactorial disorder, and the gut microbiome has been suggested to contribute to its onset. In order to better clarify the role of the microbiome in obesity, we evaluated the metatranscriptome in duodenal biopsies from a cohort of 23 adult severely obese and lean control subjects using next generation sequencing. Our aim was to provide a general picture of the duodenal metatranscriptome associated with severe obesity.

MLL4-associated condensates counterbalance Polycomb-mediated nuclear mechanical stress in Kabuki syndrome.

The genetic elements required to tune gene expression are partitioned in active and repressive nuclear condensates. Chromatin compartments include transcriptional clusters whose dynamic establishment and functioning depend on multivalent interactions occurring among transcription factors, cofactors and basal transcriptional machinery. However, how chromatin players contribute to the assembly of transcriptional condensates is poorly understood.

Correction to: Clustering analysis of tumor metabolic networks.

An amendment to this paper has been published and can be accessed via the original article.

Correction: P53-regulated miR-320a targets PDL1 and is downregulated in malignant mesothelioma.

Author Francesca Pentimalli was incorrectly associated with Histopathological Unit, IRCCS-Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy. The author's actual affiliation is Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, I-80131 Napoli, Italy.

Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.

The COrona VIrus Disease 19 (COVID-19) pandemic required the work of all global experts to tackle it. Despite the abundance of new studies, privacy laws prevent their dissemination for medical investigations: through clinical de-identification, the Protected Health Information (PHI) contained therein can be anonymized so that medical records can be shared and published. The automation of clinical de-identification through deep learning techniques has proven to be less effective for languages other than English due to the scarcity of data sets.

Pharmacological Inhibition of WEE1 Potentiates the Antitumoral Effect of the Oncolytic Virus in Malignant Mesothelioma Cell Lines.

Malignant mesothelioma (MM) is a very aggressive asbestos-related cancer, for which no therapy proves to be effective. We have recently shown that the oncolytic adenovirus had antitumor effects in MM cell lines and murine xenografts. Previous studies demonstrated that -induced host cell cycle checkpoint deregulation and consequent DNA lesions associated with the virus efficacy.

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

The 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.

P53-regulated miR-320a targets PDL1 and is downregulated in malignant mesothelioma.

Malignant pleural mesothelioma (MPM) is an aggressive cancer, related to asbestos exposure, which has a dismal prognosis. MPM diagnosis is late and often challenging, suggesting the need to identify more reliable molecular biomarkers. Here, we set out to identify differentially expressed miRNAs in epithelioid, biphasic, and sarcomatoid MPMs versus normal mesothelium and explored specific miRNA contribution to mesothelial tumorigenesis.

Clustering analysis of tumor metabolic networks.

Background: Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes.

An Algorithm for the Analysis of the 3D Spatial Organization of the Genome.

We present an algorithm, and its MATLAB implementation, based on mathematical methods to detect and localize 3D multicolor DNA FISH spots in fluorescence cell image z-stacks. This algorithm provides a method to measure the relative positioning of spots in the nucleus and inter-spot distances with the aim to enrich our understanding of the 3D spatial organization of the genome within the cell nucleus.

Targeting SRC Family Kinases in Mesothelioma: Time to Upgrade.

Malignant mesothelioma (MM) is a deadly tumor mainly caused by exposure to asbestos. Unfortunately, no current treatment is able to change significantly the natural history of the disease, which has a poor prognosis in the majority of patients. The non-receptor tyrosine kinase SRC and other SRC family kinase (SFK) members are frequently hyperactivated in many cancer types, including MM.

A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections.

We introduce a machine learning-based analysis to predict the immunohistochemical (IHC) labeling index for the cell proliferation marker Ki67/MIB1 on cancer tissues based on morphometrical features extracted from hematoxylin and eosin (H&E)-stained formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples. We provided a proof-of-concept prediction of the Ki67/MIB1 IHC positivity of cancer cells through the definition and quantitation of single nuclear features. In the first instance, we set our digital framework on Ki67/MIB1-stained OSCC (oral squamous cell carcinoma) tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, and we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then, we sorted the morphometric features of tumor cells related to their Ki67 IHC status.

Social epistemic actions.

We consider the ways humans engage in social epistemic actions, to guide each other's attention, prediction, and learning processes towards salient information, at the timescale of online social interaction and joint action. This parallels the active guidance of other's attention, prediction, and learning processes at the longer timescale of niche construction and cultural practices, as discussed in the target article.

Characterization of the Duodenal Mucosal Microbiome in Obese Adult Subjects by 16S rRNA Sequencing.

The gut microbiota may have an impact on obesity. To date, the majority of studies in obese patients reported microbiota composition in stool samples. The aim of this study was to investigate the duodenal mucosa dysbiosis in adult obese individuals from Campania, a region in Italy with a very high percentage of obese people, to highlight microbial taxa likely associated with obesity.

What's Genetic Variation Got to Do with It? Starvation-Induced Self-Fertilization Enhances Survival in Paramecium.

The pervasiveness of sex despite its well-known costs is a long-standing puzzle in evolutionary biology. Current explanations for the success of sex in nature largely rely on the adaptive significance of the new or rare genotypes that sex may generate. Less explored is the possibility that sex-underlying molecular mechanisms can enhance fitness and convey benefits to the individuals that bear the immediate costs of sex.