A multi-objective DIRECT algorithm for ship hull optimization

The paper is concerned with black-box nonlinear constrained multi-objective optimization problems. Our interest is the definition of a multi-objective deterministic partition-based algorithm. The main target of the proposed algorithm is the solution of a real ship hull optimization problem. To this purpose and in pursuit of an efficient method, we develop an hybrid algorithm by coupling a multi-objective DIRECT-type algorithm with an efficient derivative-free local algorithm. The results obtained on a set of “hard” nonlinear constrained multi-objective test problems show viability of the proposed approach. Results on a hull-form optimization of a high-speed catamaran (sailing in head waves in the North Pacific Ocean) are also presented. In order to consider a real ocean environment, stochastic sea state and speed are taken into account. The problem is formulated as a multi-objective optimization aimed at (i) the reduction of the expected value of the mean total resistance in irregular head waves, at variable speed and (ii) the increase of the ship operability, with respect to a set of motion-related constraints. We show that the hybrid method performs well also on this industrial problem

Improving P300 Speller performance by means of optimization and machine learning

Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user’s brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as stepwise linear discriminant analysis and support vector machine (SVM) are the most used discriminant algorithms for ERPs’ classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio, it also slows down the process. In the early studies, the number of iterations was fixed (no stopping environment), but recently several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met in order to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets

Porous silicon-based aptasensors: The next generation of label-free devices for health monitoring

Aptamers are artificial nucleic acid ligands identified and obtained from combinatorial libraries of synthetic nucleic acids through the in vitro process SELEX (systematic evolution of ligands by exponential enrichment). Aptamers are able to bind an ample range of non-nucleic acid targets with great specificity and affinity. Devices based on aptamers as bio-recognition elements open up a new generation of biosensors called aptasensors. This review focuses on some recent achievements in the design of advanced label-free optical aptasensors using porous silicon (PSi) as a transducer surface for the detection of pathogenic microorganisms and diagnostic molecules with high sensitivity, reliability and low limit of detection (LoD)

New G-quadruplex-forming oligodeoxynucleotides incorporating a bifunctional double-ended linker (DEL): Effects of del size and ODNs orientation on the topology, stability, and molecularity of del-G-quadruplexes

G-quadruplexes (G4s) are unusual secondary structures of DNA occurring in guanosine-rich oligodeoxynucleotide (ODN) strands that are extensively studied for their relevance to the biological processes in which they are involved. In this study, we report the synthesis of a new kind of G4-forming molecule named double-ended-linker ODN (DEL-ODN), in which two TG4T strands are attached to the two ends of symmetric, non-nucleotide linkers. Four DEL-ODNs differing for the incorporation of either a short or long linker and the directionality of the TG4T strands were synthesized, and their ability to form G4 structures and/or multimeric species was investigated by PAGE, HPLC–size-exclusion chromatography (HPLC–SEC), circular dichroism (CD), and NMR studies in comparison with the previously reported monomeric tetra-ended-linker (TEL) analogues and with the corresponding tetramolecular species (TG4T)4. The structural characterization of DEL-ODNs confirmed the formation of stable, bimolecular DEL-G4s for all DEL-ODNs, as well as of additional DEL-G4 multimers with higher molecular weights, thus suggesting a way towards the obtainment of thermally stable DNA nanostructures based on reticulated DEL-G4s

Neurocomputing for internet of things: object recognition and detection strategy

Modern and new integrated technologies have changed the traditional systems by using more advanced machine learning, artificial intelligence methods, new generation standards, and smart and intelligent devices. The new integrated networks like the Internet of Things (IoT) and 5G standards offer various benefits and services. However, these networks have suffered from multiple object detection, localization, and classification issues. Conventional Neural Networks (CNN) and their variants have been adopted for object detection, classification, and localization in IoT networks to create autonomous devices to make decisions and perform tasks without human intervention and helpful to learn in-depth features. Motivated by these facts, this paper investigates existing object detection and recognition techniques by using CNN models used in IoT networks. This paper presents a Conventional Neural Networks for 5G-Enabled Internet of Things Network (CNN-5GIoT) model for moving and static objects in IoT networks after a detailed comparison. The proposed model is evaluated with existing models to check the accuracy of real-time tracking. The proposed model is more efficient for real-time object detection and recognition than conventional methods

Silver (I) n‐heterocyclic carbene complexes: A winning and broad spectrum of antimicrobial properties

The evolution of antibacterial resistance has arisen as the main downside in fighting bacterial infections pushing researchers to develop novel, more potent and multimodal alternative drugs.Silver and its complexes have long been used as antimicrobial agents in medicine due to the lack of silver resistance and the effectiveness at low concentration as well as to their low toxicities compared to the most commonly used antibiotics.N‐Heterocyclic Carbenes (NHCs) have been extensively employed to coordinate transition metals mainly for catalytic chemistry. However, more recently, NHC ligands have been applied as carrier molecules for metals in anticancer applications. In the present study we selected from literature two NHC‐carbene based on acridinescaffoldand detailed nonclassicalpyrazole derived mono NHC‐Ag neutral and bis NHC‐Ag cationic complexes. Their inhibitor effect on bacterial strains Gram‐negative and positivewas evaluated. Imidazolium NHC silver complex containing the acridine chromophore showed effectiveness at extremely low MIC values. Although pyrazole NHC silver complexes are less active than the acridine NHC‐silver, they represent the first example of this class of compounds with antimicrobial properties. Moreover all complexesare not toxic and they show not significant activity againstmammalian cells (Hek lines) after 4 and 24 h. Based on our experimental evidence, we are confident that this promising class of complexes could represent a valuable starting point for developing candidates for the treatment of bacterial infections, delivering great effectiveness and avoiding the development of resistance mechanisms

Exploring the DNA2-PNA heterotriplex formation in targeting the Bcl-2 gene promoter: A structural insight by physico-chemical and microsecond-scale MD investigation

Peptide Nucleic Acids (PNAs) represent a promising tool for gene modulation in anticancer treatment. The uncharged peptidyl backbone and the resistance to chemical and enzymatic degradation make PNAs highly advantageous to form stable hybrid complexes with complementary DNA and RNA strands, providing higher stability than the corresponding natural analogues. Our and other groups’ research has successfully shown that tailored PNA sequences can effectively downregulate the expression of human oncogenes using antigene, antisense, or anti-miRNA approaches. Specifically, we identified a seven bases-long PNA sequence, complementary to the longer loop of the main G-quadruplex structure formed by the bcl2midG4 promoter sequence, capable of downregulating the expression of the antiapoptotic Bcl-2 protein and enhancing the anticancer activity of an oncolytic adenovirus. Here, we extended the length of the PNA probe with the aim of including the double-stranded Bcl-2 promoter among the targets of the PNA probe. Our investigation primarily focused on the structural aspects of the resulting DNA2-PNA heterotriplex that were determined by employing conventional and accelerated microsecond-scale molecular dynamics simulations and chemical-physical analysis. Additionally, we conducted preliminary biological experiments using cytotoxicity assays on human A549 and MDA-MB-436 adenocarcinoma cell lines, employing the oncolytic adenovirus delivery strategy

An intuitive introduction to the evolution of physical systems

We outline a unified introduction to the general problem of dynamics intended for a high-school students audience. The attempt consists in circumventing the lack of mathematical knowledge with the use of 1) geometric diagrams, 2) a discretized version of the equations of motion and 3) a simplified form of computation and analysis of their solutions. The aim is to allow students to approach theoretical features as well as computational aspects of the evolution equations through the use of spreadsheets, a work environment students are usually familiar with and an ideal tool for an intuitive approach to recursive algorithms. The proposal was presented to an audience composed of students of University courses of Physics teaching and to high-school Science teachers

Transcriptomics and metabolomics integration reveals redox-dependent metabolic rewiring in breast cancer cells

Rewiring glucose metabolism toward aerobic glycolysis provides cancer cells with a rapid generation of pyruvate, ATP, and NADH, while pyruvate oxidation to lactate guarantees refueling of oxidized NAD+ to sustain glycolysis. CtPB2, an NADH-dependent transcriptional co-regulator, has been proposed to work as an NADH sensor, linking metabolism to epigenetic transcriptional reprogramming. By integrating metabolomics and transcriptomics in a triple-negative human breast cancer cell line, we show that genetic and pharmacological down-regulation of CtBP2 strongly reduces cell proliferation by modulating the redox balance, nucleotide synthesis, ROS generation, and scavenging. Our data highlight the critical role of NADH in controlling the oncogene-dependent crosstalk between metabolism and the epigenetically mediated transcriptional program that sustains energetic and anabolic demands in cancer cells