Design and Implementation of a Physical Bitcoin Coin

[Abstract] One of the major factors hindering the adoption of crypto assets in general, and Bitcoin in particular, is the high level of complexity they present to the common user. Although physical coins are a possible solution, the need to place trust in the manufacturers (so that they throw away the private key) is a big drawback that has hampered their widespread use. The recent boom of the maker movement has brought in a significant number of users with access to 3D printing devices, as well as the supporting electronic and computing resources. We have taken advantage of these capabilities to develop an open source project that interested parties can use to easily print a physical model of a Bitcoin coin, along with the necessary software that allows the creation and validation of keys and addresses.Xunta de Galicia; ED431C 2018/4

System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

[Abstract] Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application

Obtendo información útil para a mellora dunha materia a partir dos resultados dos exames de resposta múltiple

[Resumo] Os procesos de avaliación, deben aplicarse ós docentes e mesmo á materia en si, non só ós alumnos. Con esta finalidade formúlase unha análise dos resultados acadados polo alumnado durante a proba de avaliación empregada na materia de Marcos de Desenvolvemento (Grao en Enxeñaría Informática – Facultade de Informática). O exame é de resposta múltiple (4 opcións por pregunta, só unha válida e restando puntos as respostas erróneas). Os exames analízanse en dúas ramas: por unha banda, estúdanse as taxas de acerto/fallo/en branco de cada unha das preguntas; por outra, a porcentaxe de opcións (a,b,c,d, branco) en cada pregunta. Este sinxelo estudo, automatizado mediante o emprego dunha folla de cálculo, permite, non obstante, obter interesantes conclusións: • Detecta ambigüidades ou erros na formulación das preguntas que, polo xeral, se derivan nunha elevada porcentaxe de respostas en branco. • Detecta lagoas de coñecemento nalgunha das áreas da materia, que orixinan preguntas cunha elevada taxa de erros. Cada pregunta está asociada a un bloque teórico, polo que se podes establecer en qué aspectos os alumnos presentan máis ou menos coñecementos. Ambos aspectos poden ser empregados para detectar erros na formulación da materia e/ou exame e facer posible así a definición de plans de mellora de cara ós vindeiros cursos académicos

The Rücker–Markov invariants of complex bio-systems: applications in parasitology and neuroinformatics

[Abstract] Rücker's walk count (WC) indices are well-known topological indices (TIs) used in Chemoinformatics to quantify the molecular structure of drugs represented by a graph in Quantitative structure–activity/property relationship (QSAR/QSPR) studies. In this work, we introduce for the first time the higher-order (kth order) analogues (WCk) of these indices using Markov chains. In addition, we report new QSPR models for large complex networks of different Bio-Systems useful in Parasitology and Neuroinformatics. The new type of QSPR models can be used for model checking to calculate numerical scores S(Lij) for links Lij (checking or re-evaluation of network connectivity) in large networks of all these fields. The method may be summarized as follows: (i) first, the WCk(j) values are calculated for all jth nodes in a complex network already created; (ii) A linear discriminant analysis (LDA) is used to seek a linear equation that discriminates connected or linked (Lij = 1) pairs of nodes experimentally confirmed from non-linked ones (Lij = 0); (iii) The new model is validated with external series of pairs of nodes; (iv) The equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. The linear QSPR models obtained yielded the following results in terms of overall test accuracy for re-construction of complex networks of different Bio-Systems: parasite–host networks (93.14%), NW Spain fasciolosis spreading networks (71.42/70.18%) and CoCoMac Brain Cortex co-activation network (86.40%). Thus, this work can contribute to the computational re-evaluation or model checking of connectivity (collation) in complex systems of any science field.Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; Ibero-NBIC, 209RT-0366Ministerio de Ciencia e Innovación; TIN2009-0770

Random Forest Classification Based on Star Graph Topological Indices for Antioxidant Proteins

[Abstract] Aging and life quality is an important research topic nowadays in areas such as life sciences, chemistry, pharmacology, etc. People live longer, and, thus, they want to spend that extra time with a better quality of life. At this regard, there exists a tiny subset of molecules in nature, named antioxidant proteins that may influence the aging process. However, testing every single protein in order to identify its properties is quite expensive and inefficient. For this reason, this work proposes a model, in which the primary structure of the protein is represented using complex network graphs that can be used to reduce the number of proteins to be tested for antioxidant biological activity. The graph obtained as a representation will help us describe the complex system by using topological indices. More specifically, in this work, Randić’s Star Networks have been used as well as the associated indices, calculated with the S2SNet tool. In order to simulate the existing proportion of antioxidant proteins in nature, a dataset containing 1999 proteins, of which 324 are antioxidant proteins, was created. Using this data as input, Star Graph Topological Indices were calculated with the S2SNet tool. These indices were then used as input to several classification techniques. Among the techniques utilised, the Random Forest has shown the best performance, achieving a score of 94% correctly classified instances. Although the target class (antioxidant proteins) represents a tiny subset inside the dataset, the proposed model is able to achieve a percentage of 81.8% correctly classified instances for this class, with a precision of 81.3%.Galicia. Consellería de Economía e Industria; 10SIN105004PRGalicia. Consellería de Economía e Industria; O9SIN010105PRMinisterio de Economía y Competitividad; TIN-2009-0770

Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

[Abstract] Early detection is crucial to prevent the progression of Alzheimer’s disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI), which are not generally used. Sagittal MRIs from ADNI and OASIS data sets were employed. Experiments were conducted using Transfer Learning (TL) techniques in order to achieve more accurate results. There are two main conclusions to be drawn from this work: first, the damages related to AD and its stages can be distinguished in sagittal MRI and, second, the results obtained using DL models with sagittal MRIs are similar to the state-of-the-art, which uses the horizontal-plane MRI. Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This could pave the way for further research. Finally, one should bear in mind that in certain fields, obtaining the examples for a data set can be very expensive. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.This work is supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute in the context of the Spanish National Plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—”A way to build Europe”. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23), Competitive Reference Groups (Ref. ED431C 2018/49) and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/4

Applying Artificial Intelligence for Operating System Fingerprinting

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] In the field of computer security, the possibility of knowing which specific version of an operating system is running behind a machine can be useful, to assist in a penetration test or monitor the devices connected to a specific network. One of the most widespread tools that better provides this functionality is Nmap, which follows a rule-based approach for this process. In this context, applying machine learning techniques seems to be a good option for addressing this task. The present work explores the strengths of different machine learning algorithms to perform operating system fingerprinting, using for that, the Nmap reference database. Moreover, some optimizations were applied to the method which brought the best results, random forest, obtaining an accuracy higher than 96%.CITIC, as a research center accredited by the Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported—80% through ERDF, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades (Grant ED431G 2019/01). This project was also supported by the “Consellería de Cultura, Educación e Ordenación Universitaria” via the Consolidation and Structuring of Competitive Research Units–Competitive Reference Groups (ED431C 2018/49) and the COST Action 17124 DigForAsp, supported by COST (European Cooperation in Science and Technology, www.cost.eu, (accessed on 25 October 2021)).Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2018/4

Molecular Docking and Machine Learning Analysis of Abemaciclib in Colon Cancer

[Abstract] Background - The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design and discovery of new specific treatments for each patient. In this context, this work offers new ways to reuse existing databases and work to create added value in research. Three published signatures with significante prognostic value in Colon Adenocarcinoma (COAD) were indentified. These signatures were combined in a new meta-signature and validated with main Machine Learning (ML) and conventional statistical techniques. In addition, a drug repurposing experiment was carried out through Molecular Docking (MD) methodology in order to identify new potential treatments in COAD. Results - The prognostic potential of the signature was validated by means of ML algorithms and differential gene expression analysis. The results obtained supported the possibility that this meta-signature could harbor genes of interest for the prognosis and treatment of COAD. We studied drug repurposing following a molecular docking (MD) analysis, where the different protein data bank (PDB) structures of the genes of the meta-signature (in total 155) were confronted with 81 anti-cancer drugs approved by the FDA. We observed four interactions of interest: GLTP - Nilotinib, PTPRN - Venetoclax, VEGFA - Venetoclax and FABP6 - Abemaciclib. The FABP6 gene and its role within different metabolic pathways were studied in tumour and normal tissue and we observed the capability of the FABP6 gene to be a therapeutic target. Our in silico results showed a significant specificity of the union of the protein products of the FABP6 gene as well as the known action of Abemaciclib as an inhibitor of the CDK4/6 protein and therefore, of the cell cycle. Conclusions - The results of our ML and differential expression experiments have first shown the FABP6 gene as a possible new cancer biomarker due to its specificity in colonic tumour tissue and no expression in healthy adjacent tissue. Next, the MD analysis showed that the drug Abemaciclib characteristic affinity for the different protein structures of the FABP6 gene. Therefore, in silico experiments have shown a new opportunity that should be validated experimentally, thus helping to reduce the cost and speed of drug screening. For these reasons, we propose the validation of the drug Abemaciclib for the treatment of colon cancer.This work was supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe.” and the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23) and Competitive Reference Groups (Ref. ED431C 2018/49). The calculations were performed on resources provided by the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER). The funding body did not have a role in the experimental design; data collection, analysis and interpretation; and writing of this manuscriptXunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/4

Bio-AIMS collection of chemoinformatics web tools based on molecular graph information and artificial intelligence models

[Abstract] The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.Red Gallega de Investigación y Desarrollo de Medicamentos; R2014/025Instituto de Salud Carlos III; PI13/0028

Mejora continua de la calidad de la docencia a partir del análisis de los resultados de evaluación

[Resumen] El objetivo de cualquier docente debería ser la mejora continua en sus materias. En este trabajo se muestra una aproximación para adecuar las enseñanzas a aquellos aspectos más necesarios dentro de una materia. Para ello es necesario tomar nota de las debilidades mostradas por el alumnado. Por lo tanto, se plantea un análisis exhaustivo del rendimiento, más allá de una simple evaluación numérica, con el objetivo de dirigir los esfuerzos docentes a las áreas en las que se detecta una mayor necesidad. Así, para valorar los conocimientos teóricos se mostrará un análisis estadístico a partir de los resultados de la prueba teórica realizada (de tipo respuesta múltiple) analizando no sólo la cantidad de fallos sino analizando dónde y en qué porcentaje se producen éstos. En relación a la práctica, se desarrolla una rúbrica que permite una corrección exhaustiva de los trabajos, dejando además abierta la posibilidad a apuntar las observaciones necesarias en todos los puntos de interés. Se contextualiza la propuesta realizada en una materia concreta (Marcos de Desarrollo), puesto que es la materia que se empleó para su puesta en marcha. Sin embargo, el método propuesto es totalmente genérico y puede ser trasladado sin apenas cambio a cualquier otra materia.[Abstract] The objective of any teaching should be the continuous improvement of the subjects. This paper shows an approach to adapt the teachings to those aspects most necessary within a subject. For this, it is necessary to take note of the weaknesses shown by the students. Therefore, an exhaustive analysis of the performance is proposed, beyond a simple numerical evaluation, with the aim of directing the teaching efforts to the areas in which a greater need is detected. Thus, to assess theoretical knowledge, statistical analysis will be shown based on the results of the theoretical test carried out (multiple response type) analyzing not only the number of failures but analyzing where and in what percentage these occur. In relation to the practice, a rubric is developed that allows an exhaustive correction of the works, leaving also open the possibility to record the necessary observations in all the points of interest. The proposal made in a specific subject (Development Frameworks) is contextualized, since it is the material used for its implementation. However, the proposed method is totally generic and can be transferred with little change to any other subject