A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN

Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out

Repurposing Study of 4-Acyl-1-phenylaminocarbonyl-2-substituted-piperazine Derivatives as Potential Anticancer Agents-In Vitro Evaluation against Breast Cancer Cells

first_pagesettingsOrder Article Reprints Open AccessArticle Repurposing Study of 4-Acyl-1-phenylaminocarbonyl-2-substituted-piperazine Derivatives as Potential Anticancer Agents—In Vitro Evaluation against Breast Cancer Cells by Emilio Guillén-Mancina 1,†,María del Rosario García-Lozano 2,3,†ORCID,Estefanía Burgos-Morón 1ORCID,Sarah Mazzotta 2,4ORCID,Pablo Martínez-Aguado 2,3,5,6,José Manuel Calderón-Montaño 1ORCID,José Manuel Vega-Pérez 2,Miguel López-Lázaro 1ORCID,Fernando Iglesias-Guerra 2,* andMargarita Vega-Holm 2,*ORCID 1 Department of Pharmacology, Faculty of Pharmacy, University of Seville, 41012 Seville, Spain 2 Department of Organic and Medicinal Chemistry, Faculty of Pharmacy, University of Seville, 41012 Seville, Spain 3 Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital, CSIC, University of Seville, 41013 Seville, Spain 4 Department of Chemistry, University of Milan, 20133 Milan, Italy 5 Infectious Diseases and Microbiology Clinical Unit, University Hospital Virgen Macarena, 41009 Seville, Spain 6 Departament of Medicine, School of Medicine, University of Seville, 41012 Seville, Spain * Authors to whom correspondence should be addressed. † These authors contributed equally to this work. Int. J. Mol. Sci. 2023, 24(23), 17041; https://doi.org/10.3390/ijms242317041 Original submission received: 31 January 2023 / Resubmission received: 3 November 2023 / Revised: 21 November 2023 / Accepted: 28 November 2023 / Published: 1 December 2023 (This article belongs to the Special Issue Novel Molecular Pathways in Oncology) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Breast cancer is the most common type of cancer in women. Although current treatments can increase patient survival, they are rarely curative when the disease is advanced (metastasis). Therefore, there is an urgent need to develop new cytotoxic drugs with a high selectivity toward cancer cells. Since repurposing approved drugs for cancer therapy has been a successful strategy in recent years, in this study, we screened a library of antiviral piperazine-derived compounds as anticancer agents. The compounds included a piperazine ring and aryl urea functions, which are privileged structures present in several anti-breast cancer drugs. The selective cytotoxic activity of a set of thirty-four 4-acyl-2-substituted piperazine urea derivatives against MCF7 breast cancer cells and MCF 10A normal breast cells was determined. Compounds 31, 32, 35, and 37 showed high selective anticancer activity against breast cancer cells and were also tested against another common type of cancer, non-small cell lung cancer (A549 lung cancer cells versus MRC-5 lung normal cells). Compounds 35 and 37 also showed selectivity against lung cancer cells. These results suggest that compounds 35 and 37 may be promising hit compounds for the development of new anticancer agents.Ministerio de Ciencia, Innovación y Universidades de España, Plan Estatal 2017-2020 - I+D+i PID2019-104767RB-I00Junta de Andalucía - 2017/CTS-657 y 2019/CTS-657Universidad de Sevilla, V Plan Propio de Investigación y Transferencia” - PPI2015-II.2Universidad de Sevilla, VI Plan Propio de Investigación y Transferencia” - VIPPIT-2019-I.5 y VIPPIT-2020-I.5Universidad de Sevilla, VII Plan Propio de Investigación y Transferencia” - VIIPPIT-2023-I.

Neutron spectrometry using artificial neural networks for a bonner sphere spectrometer with 3He detector

Neutron spectra unfolding and dose equivalent calculation are complicated tasks in radiation protection, are highly dependent of the neutron energy, and a precise knowledge on neutron spectrometry is essential for all dosimetry-related studies as well as many nuclear physics experiments. In previous works have been reported neutron spectrometry and dosimetry results, by using the ANN technology as alternative solution, starting from the count rates of a Bonner spheres system with a LiI(Eu) thermal neutrons detector, 7 polyethylene spheres and the UTA4 response matrix with 31 energy bins. In this work, an ANN was designed and optimized by using the RDANN methodology for the Bonner spheres system used at CIEMAT Spain, which is composed of a He neutron detector, 12 moderator spheres and a response matrix for 72 energy bins. For the ANN design process a neutrons spectra catalogue compiled by the IAEA was used. From this compilation, the neutrons spectra were converted from lethargy to energy spectra. Then, the resulting energy ?uence spectra were re-binned by using the MCNP code to the corresponding energy bins of the He response matrix before mentioned. With the response matrix and the re-binned spectra the counts rate of the Bonner spheres system were calculated and the resulting re-binned neutrons spectra and calculated counts rate were used as the ANN training data set

Synthesis, Optical Properties, and Antiproliferative Evaluation of NBD-Triterpene Fluorescent Probes

A fluorescent labeling protocol for hydroxylated natural compounds with promising antitumor properties has been used to synthesize 12 derivatives having fluorescent properties and biological activity. The reagent used for the synthesis of these fluorescent derivatives was 7-nitrobenzo-2-oxa-1,3-diazole chloride (NBD-Cl). The linkers employed to bind the NBD-Cl reagent to the natural compounds were ω-amino acids of different chain lengths. The natural triterpene compounds chosen were oleanolic and maslinic acid, as their corresponding 28-benzylated derivatives. Thus, triterpene conjugates with NBD have been studied for their optical fluorescence properties and their biological activities against cell proliferation in three cancer cell lines (B16-F10, HT-29, and HepG2), compared with three nontumor cell lines (HPF, IEC-18, and WRL68) from different tissues. The results of the fluorescence study have shown that the best fluorescent labels are those in which the ω-amino acid chain is shorter, and the carboxylic group is not benzylated. Analysis by confocal microscopy showed that these compounds were rapidly incorporated into cells in all three cancer cell lines, with these same derivatives showing the highest toxicity against the cancer cell lines tested. Then, the fluorescent labeling of these triterpene conjugates with NBD enabled their uptake and subcellular distribution to be followed to probe in detail their biological properties at the cellular and molecular level.Grupo de Investigación "Biotecnología y Química de Productos Naturales" (grupo FQM-139 del PAIDI de la Junta de Andalucía

A neutron spectrum unfolding code based on generalized regression artificial neural networks

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation

Generalized Regression Neural Networks with Application in Neutron Spectrometry

The aim of this research was to apply a generalized regression neural network (GRNN) to predict neutron spectrum using the rates count coming from a Bonner spheres system as the only piece of information. In the training and testing stages, a data set of 251 different types of neutron spectra, taken from the International Atomic Energy Agency compilation, were used. Fifty-one predicted spectra were analyzed at testing stage. Training and testing of GRNN were carried out in the MATLAB environment by means of a scientific and technological tool designed based on GRNN technology, which is capable of solving the neutron spectrometry problem with high performance and generalization capability. This computational tool automates the pre-processing of information, the training and testing stages, the statistical analysis, and the post-processing of the information. In this work, the performance of feed-forward backpropagation neural networks (FFBPNN) and GRNN was compared in the solution of the neutron spectrometry problem. From the results obtained, it can be observed that despite very similar results, GRNN performs better than FFBPNN because the former could be used as an alternative procedure in neutron spectrum unfolding methodologies with high performance and accuracy

The immunogenetic diversity of the HLA system in Mexico correlates with underlying population genetic structure

We studied HLA class I (HLA-A, -B) and class II (HLA-DRB1, -DQB1) allele groups and alleles by PCR-SSP based typing in a total of 15,318 mixed ancestry Mexicans from all the states of the country divided into 78 sample sets, providing information regarding allelic and haplotypic frequencies and their linkage disequilibrium, as well as admixture estimates and genetic substructure. We identified the presence of 4268 unique HLA extended haplotypes across Mexico and find that the ten most frequent (HF > 1%) HLA haplotypes with significant linkage disequilibrium (Δ’≥0.1) in Mexico (accounting for 20% of the haplotypic diversity of the country) are of primarily Native American ancestry (A*02~B*39~DRB1*04~DQB1*03:02, A*02~B*35~DRB1*08~DQB1*04, A*68~B*39~DRB1*04~DQB1*03:02, A*02~B*35~DRB1*04~DQB1*03:02, A*24~B*39~DRB1*14~DQB1*03:01, A*24~B*35~DRB1*04~DQB1*03:02, A*24~B*39~DRB1*04~DQB1*03:02, A*02~B*40:02~DRB1*04~DQB1*03:02, A*68~B*35~DRB1*04~DQB1*03:02, A*02~B*15:01~DRB1*04~DQB1*03:02). Admixture estimates obtained by a maximum likelihood method using HLA-A/-B/-DRB1 as genetic estimators revealed that the main genetic components in Mexico as a whole are Native American (ranging from 37.8% in the northern part of the country to 81.5% in the southeastern region) and European (ranging from 11.5% in the southeast to 62.6% in northern Mexico). African admixture ranged from 0.0 to 12.7% not following any specific pattern. We were able to detect three major immunogenetic clusters correlating with genetic diversity and differential admixture within Mexico: North, Central and Southeast, which is in accordance with previous reports using genome-wide data. Our findings provide insights into the population immunogenetic substructure of the whole country and add to the knowledge of mixed ancestry Latin American population genetics, important for disease association studies, detection of demographic signatures on population variation and improved allocation of public health resources.Fil: Barquera, Rodrigo. Max Planck Institute For The Science Of Human History; Alemania. Instituto Nacional de Antropología E Historia. Escuela Nacional de Antropología E Historia; MéxicoFil: Hernández Zaragoza, Diana Iraíz. Técnicas Genéticas Aplicadas A la Clínica (tgac); México. Instituto Nacional de Antropología E Historia. Escuela Nacional de Antropología E Historia; MéxicoFil: Bravo Acevedo, Alicia. Instituto Mexicano del Seguro Social; MéxicoFil: Arrieta Bolaños, Esteban. Universitat Essen; AlemaniaFil: Clayton, Stephen. Max Planck Institute For The Science Of Human History; AlemaniaFil: Acuña Alonzo, Víctor. Instituto Nacional de Antropología E Historia, Mexico; MéxicoFil: Martínez Álvarez, Julio César. Instituto Mexicano del Seguro Social; MéxicoFil: López Gil, Concepción. Instituto Mexicano del Seguro Social; MéxicoFil: Adalid Sáinz, Carmen. Instituto Mexicano del Seguro Social; MéxicoFil: Vega Martínez, María del Rosario. Hospital Central Sur de Alta Especialidad; MéxicoFil: Escobedo Ruíz, Araceli. Instituto Mexicano del Seguro Social; MéxicoFil: Juárez Cortés, Eva Dolores. Instituto Mexicano del Seguro Social; MéxicoFil: Immel, Alexander. Max Planck Institute For The Science Of Human History; Alemania. Christian Albrechts Universitat Zu Kiel; AlemaniaFil: Pacheco Ubaldo, Hanna. Instituto Nacional de Antropología E Historia. Escuela Nacional de Antropología E Historia; MéxicoFil: González Medina, Liliana. Instituto Nacional de Antropología E Historia. Escuela Nacional de Antropología E Historia; MéxicoFil: Lona Sánchez, Abraham. Instituto Nacional de Antropología E Historia. Escuela Nacional de Antropología E Historia; MéxicoFil: Lara Riegos, Julio. Universidad Autónoma de Yucatán; MéxicoFil: Sánchez Fernández, María Guadalupe de Jesús. Instituto Mexicano del Seguro Social; MéxicoFil: Díaz López, Rosario. Hospital Central Militar, Mexico City; MéxicoFil: Guizar López, Gregorio Ulises. Hospital Central Militar, Mexico City; MéxicoFil: Medina Escobedo, Carolina Elizabeth. Instituto Mexicano del Seguro Social; MéxicoFil: Arrazola García, María Araceli. Instituto Mexicano del Seguro Social; MéxicoFil: Montiel Hernández, Gustavo Daniel. Instituto Nacional de Antropología E Historia. Escuela Nacional de Antropología E Historia; MéxicoFil: Hernández Hernández, Ofelia. Técnicas Genéticas Aplicadas a la Clínica ; MéxicoFil: Ramos de la Cruz, Flor del Rocío. Instituto Mexicano del Seguro Social; MéxicoFil: Juárez Nicolás, Francisco. Instituto Nacional de Pediatría; MéxicoFil: Pantoja Torres, Jorge Arturo. Instituto Mexicano del Seguro Social; MéxicoFil: Rodríguez Munguía, Tirzo Jesús. Hospital General Norberto Treviño Zapata; MéxicoFil: Juárez Barreto, Vicencio. Hospital Infantil de Mexico Federico Gomez; MéxicoFil: Gonzalez-Jose, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentin

Laboratorio en abierto: aPrendiendo a CopiaR el ADN

El objetivo principal del proyecto es la puesta a punto de recursos educativos en abierto (REA) dirigidos a los alumnos de secundaria. El punto de partida será plantear diferentes retos y situaciones que se pueden resolver utilizando distintos recursos científicos, para decidir qué recurso es el más adecuado y cómo se aplica. En esta propuesta la resolución de los problemas planteados estaría basada en la aplicación de una herramienta que ha revolucionado la genética y biología, la reacción en Cadena de la Polimerasa, conocida como PCR

Volteando la tortilla. Género y maíz en la alimentación actual de México.

Ante escenarios complejos, patriarcales y desoladores que dejan ver el neoliberalismo, la globalización agroalimentaria, el calentamiento global y las contaminaciones de granos nativos por la imposición de transgénicos, nos cuestionamos si existen algunas alternativas para preservar el maíz nativo como un recurso multiestratégico (alimentario, económico, cultural, ecológico y tecnológico) tomando en cuenta las condiciones actuales de desigualdades sociales de género, etnia, clases y edad que predominan en el campo mexicano. Para responder a algunos cuestionamientos, este libro presenta algunas alternativas a través de diversas experiencias femeninas y de relaciones de género en torno al maíz y la alimentación. Todas ellas muestran que es posible construir una masa crítica para salvaguardar el maíz nativo bajo esas condiciones desoladoras, pero siempre y cuando se “voltee la tortilla”, metáfora que da pie al inicio de otra realidad humanizada y en sincronía con la naturaleza.Proyecto realizado con financiamiento Conacy