Artificial intelligence and network science as tools to illustrate academic research evolution in interdisciplinary fields: The case of Italian design.

In this paper, we explore the application of Artificial Intelligence and network science methodologies in characterizing interdisciplinary disciplines, with a specific focus on the field of Italian design, taken as a paradigmatic example. Exploratory data analysis and the study of academic collaboration networks highlight how the field is evolving towards increased collaboration. Text analysis and semantic topic modelling identified the evolution of research interest over time, defining a ranking of pairs of keywords and three prominent research topics: User-Centric Experience Design, Innovative Product Design and Sustainable Service Design.

Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices.

Introduction: Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in , and is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML.

Socio-ecological Challenges and Adaptation Strategies of Farmers Towards Changing Climate in Vindhyan highlands, India.

The Indian agriculture is highly vulnerable to climate change which adversely affects crop production and livelihood of farmers. The effect is more intensified in Vindhyan highlands, where prolong drought and high rate of poverty exist in depredation environment. In this context, present study conducted to explore farmers' perception, attitude about climate change and adaptive capabilities.

Large Orbital Moment of Two Coupled Spin-Half Co Ions in a Complex on Gold.

The magnetic properties of transition-metal ions are generally described by the atomic spins of the ions and their exchange coupling. The orbital moment, usually largely quenched due the ligand field, is then seen as a perturbation. In such a scheme, = 1/2 ions are predicted to be isotropic.

An overview of data integration in neuroscience with focus on Alzheimer's Disease.

This work represents the first attempt to provide an overview of how to face data integration as the result of a dialogue between neuroscientists and computer scientists. Indeed, data integration is fundamental for studying complex multifactorial diseases, such as the neurodegenerative diseases. This work aims at warning the readers of common pitfalls and critical issues in both medical and data science fields.

A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models.

Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in preclinical practice, histological data typically involve less than 1% of total lung volume and are not amenable to longitudinal studies. A miniaturized version of computed tomography (µCT) has been introduced to radiologically examine lung in preclinical murine models of PF.

Slow Magnetic Relaxation of Dy Adatoms with In-Plane Magnetic Anisotropy on a Two-Dimensional Electron Gas.

We report on the magnetic properties of Dy atoms adsorbed on the (001) surface of SrTiO. X-ray magnetic circular dichroism reveals slow relaxation of the Dy magnetization on a time scale of about 800 s at 2.5 K, unusually associated with an easy-plane magnetic anisotropy.

Mapping Orbital-Resolved Magnetism in Single Lanthanide Atoms.

Single lanthanide atoms and molecules are promising candidates for atomic data storage and quantum logic due to the long lifetime of their magnetic quantum states. Accessing and controlling these states through electrical transport requires precise knowledge of their electronic configuration at the level of individual atomic orbitals, especially of the outer shells involved in transport. However, no experimental techniques have so far shown the required sensitivity to probe single atoms with orbital selectivity.

Where Do We Stand in Regularization for Life Science Studies?

More and more biologists and bioinformaticians turn to machine learning to analyze large amounts of data. In this context, it is crucial to understand which is the most suitable data analysis pipeline for achieving reliable results. This process may be challenging, due to a variety of factors, the most crucial ones being the data type and the general goal of the analysis (e.

Thiourea supplementation mediated reduction of grain arsenic in rice (Oryza sativa L.) cultivars: A two year field study.

The present study delineates the interactions of arsenic (As), a carcinogenic metalloid, and thiourea (TU), a non-physiological reactive oxygen species (ROS) scavenger, in rice plants grown in As contaminated fields in West Bengal, India. The study was performed for four consecutive seasons (two boro and two aman) in 2016 and 2017 with two local rice cultivars; Gosai and Satabdi (IET-4786) in a control and two As contaminated experimental fields. Thiourea (0.

Topologization of β-antimonene on BiSe via proximity effects.

Topological surface states usually emerge at the boundary between a topological and a conventional insulator. Their precise physical character and spatial localization depend on the complex interplay between the chemical, structural and electronic properties of the two insulators in contact. Using a lattice-matched heterointerface of single and double bilayers of β-antimonene and bismuth selenide, we perform a comprehensive experimental and theoretical study of the chiral surface states by means of microscopy and spectroscopic measurements complemented by first-principles calculations.

A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease.

Genome-wide association studies (GWAS) have revealed a plethora of putative susceptibility genes for Alzheimer's disease (AD), with the sole exception of APOE gene unequivocally validated in independent study. Considering that the etiology of complex diseases like AD could depend on functional multiple genes interaction network, here we proposed an alternative GWAS analysis strategy based on (i) multivariate methods and on a (ii) telescope approach, in order to guarantee the identification of correlated variables, and reveal their connections at three biological connected levels. Specifically as multivariate methods, we employed two machine learning algorithms and a genetic association test and we considered SNPs, Genes and Pathways features in the analysis of two public GWAS dataset (ADNI-1 and ADNI-2).

Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease Linked.

Aims: The purpose of this work is to find the gut microbial fingerprinting of pediatric patients with type 1 diabetes.

Methods: The microbiome of 31 children with type 1 diabetes at onset and of 25 healthy children was determined using multiple polymorphic regions of the 16S ribosomal RNA. We performed machine-learning analyses and metagenome functional analysis to identify significant taxa and their metabolic pathways content.

Transplantation Induces Profound Changes in the Transcriptional Asset of Hematopoietic Stem Cells: Identification of Specific Signatures Using Machine Learning Techniques.

During the phase of proliferation needed for hematopoietic reconstitution following transplantation, hematopoietic stem/progenitor cells (HSPC) must express genes involved in stem cell self-renewal. We investigated the expression of genes relevant for self-renewal and expansion of HSPC (operationally defined as CD34+ cells) in steady state and after transplantation. Specifically, we evaluated the expression of ninety-one genes that were analyzed by real-time PCR in CD34+ cells isolated from (i) 12 samples from umbilical cord blood (UCB); (ii) 15 samples from bone marrow healthy donors; (iii) 13 samples from bone marrow after umbilical cord blood transplant (UCBT); and (iv) 29 samples from patients after transplantation with adult hematopoietic cells.

Molecular Approach for Engineering Interfacial Interactions in Magnetic/Topological Insulator Heterostructures.

Controlling interfacial interactions in magnetic/topological insulator heterostructures is a major challenge for the emergence of novel spin-dependent electronic phenomena. As for any rational design of heterostructures that rely on proximity effects, one should ideally retain the overall properties of each component while tuning interactions at the interface. However, in most inorganic interfaces, interactions are too strong, consequently perturbing, and even quenching, both the magnetic moment and the topological surface states at each side of the interface.

Secondary Somatic Mutations in G-Protein-Related Pathways and Mutation Signatures in Uveal Melanoma.

Background: Uveal melanoma (UM), a rare cancer of the eye, is characterized by initiating mutations in the genes G-protein subunit alpha Q (), G-protein subunit alpha 11 (), cysteinyl leukotriene receptor 2 (), and phospholipase C beta 4 () and by metastasis-promoting mutations in the genes splicing factor 3B1 (), serine and arginine rich splicing factor 2 (SRSF2), and BRCA1-associated protein 1 (). Here, we tested the hypothesis that additional mutations, though occurring in only a few cases ("secondary drivers"), might influence tumor development.

Methods: We analyzed all the 4125 mutations detected in exome sequencing datasets, comprising a total of 139 Ums, and tested the enrichment of secondary drivers in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that also contained the initiating mutations.

The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach.

Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsing-remitting (RR) and secondary progressive (SP) form of multiple sclerosis (MS), to promptly identifying information useful to predict disease progression. For our analysis, a dataset of 3398 evaluations from 810 persons with MS (PwMS) was adopted.

Arsenic mitigation in rice grain loading via alternative irrigation by proposed water management practices.

Over the past three decades, the occurrence of high concentrations of arsenic (As) in drinking-water and its subsequent poisoning in rice has been recognized as a major public-health concern globally, especially in Ganga Delta Plain with more than 80 million peoples in serious As exposure far beyond than its allowable limit. An extensive field study was conducted for consecutive four years viz. 2013 to 2016, introducing a process of intermittent irrigation pattern comparing to the conventional practice of rice cultivation in India.

Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network.

Introduction: The first line of treatment for people with Diabetes mellitus is metformin. However, over the course of the disease metformin may fail to achieve appropriate glycemic control, and a second-line therapy may become necessary. In this paper we introduce Tangle, a time span-guided neural attention model that can accurately and timely predict the upcoming need for a second-line diabetes therapy from administrative data in the Australian adult population.

Different Features of Tumor-Associated NK Cells in Patients With Low-Grade or High-Grade Peritoneal Carcinomatosis.

Peritoneal carcinomatosis (PC) is a rare disease defined as diffused implantation of neoplastic cells in the peritoneal cavity. This clinical picture occurs during the evolution of peritoneal tumors, and it is the main cause of morbidity and mortality of patients affected by these pathologies, though cytoreductive surgery with heated intra-peritoneal chemotherapy (CRS/HIPEC) is yielding promising results. In the present study, we evaluated whether the tumor microenvironment of low-grade and high-grade PC could affect the phenotypic and functional features and thus the anti-tumor potential of NK cells.

Discrete Changes in Glucose Metabolism Define Aging.

Aging is a physiological process in which multifactorial processes determine a progressive decline. Several alterations contribute to the aging process, including telomere shortening, oxidative stress, deregulated autophagy and epigenetic modifications. In some cases, these alterations are so linked with the aging process that it is possible predict the age of a person on the basis of the modification of one specific pathway, as proposed by Horwath and his aging clock based on DNA methylation.

Ultra-structure alteration via enhanced silicon uptake in arsenic stressed rice cultivars under intermittent irrigation practices in Bengal delta basin.

The study implements a periodical intermittent water cycle during rice cultivation providing insight potential in minimizing soil bio-available arsenic. Soil As concentrations were 34 ± 0.49 and 72.

An assessment of arsenic hazard in groundwater-soil-rice system in two villages of Nadia district, West Bengal, India.

The present study measured arsenic (As) concentrations in soil, groundwater and rice grain samples in two villages, Sarapur and Chinili, under Chakdaha block, Nadia district, West Bengal, India. This study also included a survey of the two villages to understand the knowledge among villagers about the As problem. Soil and groundwater samples were collected from fields in two villages while rice grain samples were collected from villagers' houses.