Search results for: A. Beltran

Authors: Yuli Paola Cifuentes Sanabria, Lina Paola Beltrán Beltrán, Leonardo Juan Ramírez López

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The availability to deploy mobile applications for healthcare is increasing daily thru different mobile app stores. But within these capabilities the number of hacking attacks has also increased, in particular into medical mobile applications. The security vulnerabilities in medical mobile apps can be triggered by errors in code, incorrect logic, poor design, among other parameters. This is usually used by malicious attackers to steal or modify the users’ information. The aim of this research is to analyze the vulnerabilities detected in mobile medical apps according to risk factor standards defined by OWASP in 2014.

Keywords: mHealth apps, OWASP, protocols, security vulnerabilities, risk factors

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Authors: Javier López

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This Paper presents the evaluation model designed for the virtual education branch of The “Universidad Manuela Beltrán, Bogotá-Colombia”. This was the result of a research, developed as a case study, which had three stages: Document review, observation, and a perception survey for teachers. In the present model, the evaluation is a cross-cutting issue to the educational process. Therefore, it consists in a group of actions and guidelines which lead to analyze the student’s learning process from the admission, during the academic training, and to the graduation. This model contributes to the evaluation components which might interest other educational institutions or might offer methodological guidance to consolidate an own model

Keywords: model, evaluation, virtual education, learning process

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Authors: Juan José Milón Guzmán, Herbert Jesús Del Carpio Beltrán, Sergio Leal Braga

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Visualizations of ice particles of ice slurry are performed. The form and size of ice particles is investigated by optical microscopy. It permits to evaluate statistically the geometrical shapes of the ice crystals. The observed particle size corresponds with the different solutes (sugar, salt, propylene glycol).

Keywords: ice slurry, visualization, ice particles, solutes

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Authors: Cristian Chacha, David Asiain, Jesús Ponce de León, José Ramón Beltrán

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This work explores and characterizes the behavior of the AFE AD5941 in impedance measurement using an embedded algorithm with a constant current AC source. The main aim of this research is to improve the exact measurement of impedance values for their application in EDA-focused wearable devices. Through comprehensive study and characterization, it has been observed that employing a measurement sequence with a constant current source produces results with increased dispersion but higher accuracy. As a result, this approach leads to a more accurate system for impedance measurement.

Keywords: EDA, constant current AC source, wearable, precision, accuracy, impedance

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Authors: Juan Bojórquez, F. de Jesús Merino, Edén Bojórquez, Mario Llanez-Tizoc, Federico Valenzuela-Beltrán, Mario R. Flores, J. Ramón Gaxiola-Camacho, Henry Reyes

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As urban populations continue to grow, the demand for higher housing density in large cities has led to increased use of slender buildings to maximize limited land availability. However, structures with high slenderness ratios face significant challenges related to their resistance capacity and lateral stiffness, particularly in seismic conditions. This study evaluates the seismic behavior of four reinforced concrete frame buildings with varying slenderness ratios situated on soft soil in Mexico City. Utilizing step-by-step nonlinear dynamic analysis, the research compares the seismic performance of these buildings, presenting detailed results, conclusions, and recommendations for enhancing the earthquake resistance of slender structures.

Keywords: dynamic analysis, reinforced concrete buildings, seismic behavior, slenderness ratio

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Authors: Noé D. Lazos-Gallardo, Sonia E. Ruiz, Federico Valenzuela-Beltran

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A simplified mathematical expression to estimate ductility reduction factors of the displacement spectra corresponding to the soft soil zone of Mexico City is proposed. The aim is to allow a better characterization of the displacement spectra and provide a simple expression to be used in displacement based design (DBD). Emphasis is on the Mexico City Building Code. The study is based on the analysis of single degree of freedom (SDOF) systems with elasto-plastic hysteretic behavior. Several seismic ground motions corresponding to subduction events with magnitudes equal to or greater than 6 and recorded in different stations of Mexico City are used. The proposed expression involves the ratio of elastic and inelastic pseudo-aceleration spectra, and depends on factors such the ductility demand and the vibration period of the structural system. The resulting ductility reduction factors obtained in this study are compared with others existing in the literature, and their advantages and disadvantages are discussed.

Keywords: displacement based design, displacements spectrum, ductility reduction factors, soft soil

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Authors: Federico Valenzuela-Beltran, Sonia E. Ruiz, Alfredo Reyes-Salazar, Juan Bojorquez

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A reliability-based methodology which uses structural demand hazard curves to consider the increment of the ductility demands of structures with tilting is proposed. The approach considers the effect of two orthogonal components of the ground motions as well as the influence of soil-structure interaction. The approach involves the calculation of ductility demand hazard curves for symmetric systems and, alternatively, for systems with different degrees of asymmetry. To get this objective, demand hazard curves corresponding to different global ductility demands of the systems are calculated. Next, Uniform Exceedance Rate Spectra (UERS) are developed for a specific mean annual rate of exceedance value. Ratios between UERS corresponding to asymmetric and to symmetric systems located in soft soil of the valley of Mexico are obtained. Results indicate that the ductility demands corresponding to tilted structures may be several times higher than those corresponding to symmetric structures, depending on several factors such as tilting angle and vibration period of structure and soil.

Keywords: asymmetric yielding, seismic performance, structural reliability, tilted structures

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Authors: Alfredo Reyes-Salazar, Mario D. Llanes-Tizoc, Eden Bojorquez, Federico Valenzuela-Beltran, Juan Bojorquez, Jose R. Gaxiola-Camacho, Achintya Haldar

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Seismic analysis of steel buildings is usually based on the use of the concentrated mass (ML) matrix and the Rayleigh damping matrix (C). Similarly, the initial stiffness matrix (KO) and the first two modes associated with lateral vibrations are commonly used to develop matrix C. The evaluation of the accuracy of these practices for the particular case of steel buildings with moment-resisting steel frames constitutes the main objective of this research. For this, the non-linear seismic responses of three models of steel frames, representing low-, medium- and high-rise steel buildings, are considered. Results indicate that if the ML matrix is used, shears and bending moments in columns are underestimated by up to 30% and 65%, respectively when compared to the corresponding results obtained with the consistent mass matrix (MC). It is also shown that if KO is used in C instead of the tangent stiffness matrix (Kt), axial loads in columns are underestimated by up to 80%. It is concluded that the consistent mass matrix should be used in the structural modelling of moment-resisting steel frames and that the tangent stiffness matrix should be used to develop the Rayleigh damping matrix.

Keywords: moment-resisting steel frames, consistent and concentrated mass matrices, non-linear seismic response, Rayleigh damping

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Authors: Jarel Elgin Tolentino, Arby Denise Nera, Mary Rose Roco, Angela Vianca Aspa, Nikko Beltran, Else Dapat

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Drug resistance by cells has been the problem in the medical field for decades now. The use of medicinal plants as a source of creating powerful drugs has been nowadays recognized worldwide to treat such resistant diseases. In the present study, the potential for Diospyros philippinensis A. DC. to inhibit growth of both bacteria and cancer cell line was conducted. The leaf crude extracts were screened for the presence of phytochemicals and examined for potential bioactivities by employing several assays like Kirby-Bauer disc diffusion method, DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) assay and MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) tetrazolium assay for the antibacterial, antioxidant and cytotoxic activities of the extract, respectively. Phytochemical test results of the extracts revealed the presence of alkaloids, flavonoids, saponins, phenols, quinones, cardiac glycosides, phlobatannins, carbohydrate, cardenolides and proteins. The leaf extracts were found to exhibit antibacterial activity against gram-positive bacteria, high antioxidant activity (99.22% ± 0.005) but did not show any sign of cytotoxicity towards HCT116 (ATCC CCL-247). The study therefore concludes that D. philippinensis A. DC. leaf extract can be a source of antibacterial and chemopreventive agents. This claim may be used as basis for future investigation.

Keywords: bioassay, medicinal plants, plant crude extracts, phytochemical screening

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Authors: Claudia Patricia Beltran Miranda, Mónica López Anaya, Mónica Navarro Meza, Maria Leonor Valderrama Chairez

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Background: Mexico occupies the second world-wide place of morbid obese people (10- 12 million). Factors that predispose the development of MO are genetic, environmental, physiological, psycho-social and behavioral (nourishing habits). Objective: Associate Gln223Arg polymorphism of gene LEPR, levels of leptina and nourishing habits with the presence of morbid obesity in adolescents of the south of Jalisco (México). Methods: In 41 adolescents (18 normal weight and 23 morbid obesity) of 12 to 19 years of age, both sexes that were measure size and weight with tanita scale and stadimeter to determine IMC. Morbid obesity was determined by tables of the WHO and was established with a standard deviation >3. The Gln223Arg polymorphism have been identify by PCR and leptina levels by ELISA. Nourishing habits were evaluate by the questionnaire the Adolescent Food Habits Checklist. The statistical analysis was performed to compare mean scores obtained from the questionnaire when we compare morbid obesity vs. normal weight adolescents with p=0.03 and a significance of 95%. Results: frequencies alellics and genics were not stadistics significatives p= 0,011 and p=0,279 respectly when were compared between normal weight adolescents and morbib obesity Leptin levels and nourishing habits were associated with morbid obesity. The polymorphism not shown significance with morbid obesity. Conclusions: Dietary habits and leptin levels in adolescents are important factor that predisposes the development of obesity in adolescents. The presence of the polymorphism are not associated with morbid obesity in these subjects.

Keywords: leptin, nourishing habits, morbid obesity, polymorphism

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Authors: F. Deng, R. Sanchez, A. Beltran, S. Maestre

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The high nutritional value associated with seafood is related to the presence of essential trace elements. Moreover, seafood is considered an important source of energy, proteins, and long-chain polyunsaturated fatty acids. Generally, seafood is consumed cooked. Consequently, the nutritional value could be degraded. Seafood, such as fish, shellfish, and seaweed, could be considered as one of the main iodine sources. The deficient or excessive consumption of iodine could cause dysfunction and pathologies related to the thyroid gland. The main objective of this work is to evaluated iodine stability in hake (Merluccius) undergone different culinary techniques. The culinary process considered were: boiling, steaming, microwave cooking, baking, cooking en papillote (twisted cover with the shape of a sweet wrapper) and coating with a batter of flour and deep-frying. The determination of iodine was carried by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Regarding sample handling strategies, liquid-liquid extraction has demonstrated to be a powerful pre-concentration and clean-up approach for trace metal analysis by ICP techniques. Extraction with tetramethylammonium hydroxide (TMAH reagent) was used as a sample preparation method in this work. Based on the results, it can be concluded that the stability of iodine was degraded with the cooking processes. The major degradation was observed for the boiling and microwave cooking processes. The content of iodine in hake decreased up to 60% and 52%, respectively. However, if the boiling cooking liquid is preserved, this loss that has been generated during cooking is reduced. Only when the fish was cooked by following the cooking en papillote process the iodine content was preserved.

Keywords: cooking process, ICP-MS, iodine, hake

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Authors: Astorga-Trejo Rebeca, Fonseca-Peralta Héctor Manuel, Beltrán-Arredondo Laura Ivonne, Castro-Martínez Claudia

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The use of biomass as feedstock for the production of fuels and other chemicals of interest is an ever-growing accepted option in the way to the development of biorefinery complexes; in the Mexican state of Sinaloa, two million tons of residues from corn crops are produced every year, most of which can be converted to bioethanol and other products through biotechnological conversion using yeast and other microorganisms. Therefore, the objective of this work was to take advantage of corn stover and evaluate its potential as a substrate for the production of second-generation bioethanol (2G), enzymes, and xylitol. To produce bioethanol 2G, an acid-alkaline pretreatment was carried out prior to saccharification and fermentation. The microorganisms used for the production of enzymes, as well as for the production of xylitol, were isolated and characterized in our workgroup. Statistical analysis was performed using Design Expert version 11.0. The results showed that it is possible to obtain 2G bioethanol employing corn stover as a carbon source and Saccharomyces cerevisiae ItVer01 and Candida intermedia CBE002 with yields of 0.42 g and 0.31 g, respectively. It was also shown that C. intermedia has the ability to produce xylitol with a good yield (0.46 g/g). On the other hand, qualitative and quantitative studies showed that the native strains of Fusarium equiseti (0.4 IU/mL - xylanase), Bacillus velezensis (1.2 IU/mL – xylanase and 0.4 UI/mL - amylase) and Penicillium funiculosum (1.5 IU / mL - cellulases) have the capacity to produce xylanases, amylases or cellulases using corn stover as raw material. This study allowed us to demonstrate that it is possible to use corn stover as a carbon source, a low-cost raw material with high availability in our country, to obtain bioproducts of industrial interest, using processes that are more environmentally friendly and sustainable. It is necessary to continue the optimization of each bioprocess.

Keywords: biomass, corn stover, biorefinery, bioethanol 2G, enzymes, xylitol

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Authors: Daniel Llanes, Alfredo Reyes, Sonia E. Ruiz, Federico Valenzuela Beltran

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Steel frames have been used in building construction for more than one hundred years. Beam-column may be connected to columns using either stiffened or unstiffened angles at the top and bottom beam flanges. Designers often assume that these assemblies acted as “pinned” connections for gravity loads and that the stiffened connections would act as “fixed” connections for lateral loads. Observation of damages sustained by buildings during the 1994 Northridge earthquake indicated that, contrary to the intended behavior, in many cases, brittle fractures initiated within the connections at very low levels of plastic demand, and in some cases, while the structures remained essentially elastic. Due to the damage presented in these buildings other type of alternative connections have been proposed. According to a research funded by the Federal Emergency Management Agency (FEMA), the screwed connections have better performance when they are subjected to cyclic loads, but at the same time, these connections have some degree of flexibility. Due to this situation, some researchers ventured into the study of semi-rigid connections. In the present study three steel buildings, constituted by regular frames are analyzed. Two types of connections are considered: pinned and semi-rigid connections. With the aim to estimate their structural capacity, a number of incremental dynamic analyzes are performed. 3D structural models are used for the analyses. The seismic ground motions were recorded on sites near Los Angeles, California, where the structures are supposed to be located. The vulnerability curves of the building are obtained in terms of maximum inter-story drifts. The vulnerability curves (which correspond to the models with two different types of connections) are compared, and its implications on its structural design and performance is discussed.

Keywords: steel frame Buildings, vulnerability curves, semi-rigid connections, pinned connections

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Authors: M. Rojo, M. Blanco, T. Lacruz, S. Solano, L. Beltran, M. Graell, A. R. Sepulveda.

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There is an association between an early accumulation of Stressful Life Events (SLE) during childhood and various physical and psychological health complications. However, there are only a few studies on this topic in children and adolescents with overweight or obesity. The general aim of the study was to evaluate the accumulation and type of SLE in 200 children from 8 to 12 years old and analyze the relationship with their emotional well-being and weight status (obesity, overweight and normal weight). The children and their families completed an interview. The evaluated variables that are included in this study are sociodemographic measures, medical/psychological history, anthropometric measures (BMI, z-BMI), and psychological variables (children's clinical interview K-SADS-PL(Schedule for Affective Disorders and Schizophrenia for School-Age Children Present and Lifetime Version) and battery of questionnaires). Results: Children with overweight and obesity accumulate more stressful events from an early age and have a significantly higher percentage of psychiatric diagnoses, compared to their peers with normal weight. Presenting a child psychiatric disorder is related to greater z-BMI and the total number of SLE (p < 0.001). A higher z-BMI is also related to a greater number of stressful events during childhood. There is also a positive and significant relationship between the total number of SLE and worse emotional well-being (higher levels of anxious and depressive symptoms and low self-esteem of children) (p < 0.01). Conclusion: Children with overweight and obesity grow up in a family, school, and social context where more stressors are accumulated. This is also directly associated with worse emotional well-being. It is necessary to implement multidisciplinary prevention and intervention strategies in different changes (school, family, and health). This study is included in a project funded by the Ministry of Innovation and Science (PSI2011-23127).

Keywords: childhood obesity, emotional well-being, psychopathology, stressful life events

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Authors: Y. Zheng, M. Busi, A. F. Pedersen, M. A. Beltran, C. Gundlach

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X-ray phase contrast imaging has shown to yield a better contrast compared to conventional attenuation X-ray imaging, especially for soft tissues in the medical imaging energy range. This can potentially lead to better diagnosis for patients. However, phase contrast imaging has mainly been performed using highly brilliant Synchrotron radiation, as it requires high coherence X-rays. Many research teams have demonstrated that it is also feasible using a laboratory source, bringing it one step closer to clinical use. Nevertheless, the requirement of fine gratings and high precision stepping motors when using a laboratory source prevents it from being widely used. Recently, a random phase object has been proposed as an analyzer. This method requires a much less robust experimental setup. However, previous studies were done using a particular X-ray source (liquid-metal jet micro-focus source) or high precision motors for stepping. We have been working on a much simpler setup with just small modification of a commercial bench-top micro-CT (computed tomography) scanner, by introducing a piece of sandpaper as the phase analyzer in front of the X-ray source. However, it needs a suitable algorithm for speckle tracking and 3D reconstructions. The precision and sensitivity of speckle tracking algorithm determine the resolution of the system, while the 3D reconstruction algorithm will affect the minimum number of projections required, thus limiting the temporal resolution. As phase contrast imaging methods usually require much longer exposure time than traditional absorption based X-ray imaging technologies, a dynamic phase contrast micro-CT with a high temporal resolution is particularly challenging. Different reconstruction methods, including neural network based techniques, will be evaluated in this project to increase the temporal resolution of the phase contrast micro-CT. A Monte Carlo ray tracing simulation (McXtrace) was used to generate a large dataset to train the neural network, in order to address the issue that neural networks require large amount of training data to get high-quality reconstructions.

Keywords: micro-ct, neural networks, reconstruction, speckle-based x-ray phase contrast

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Authors: Ana Beltran Sanahuja, A. Valdés García, Saray Lopez De Pablo Gallego, Maria Soledad Prats Moya

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Tomato consumption has greatly increased worldwide in the last few years, mostly due to a growing demand for products like sofrito. In this sense, regular consumption of tomato-based products has been consistently associated with a reduction in the incidence of chronic degenerative diseases. The sofrito is a homemade tomato sauce typical of the Mediterranean area, which contains as main ingredients: tomato, onion, garlic and olive oil. There are also sofrito’s variations by adding other spices which bring at the same time not only color, flavor, smell and or aroma; they also provide medicinal properties, due to their antioxidant power. This protective effect has mainly been attributed to the predominant bioactive compounds present in sofrito, such as lycopene and other carotenoids as well as more than 40 different polyphenols. Regarding the cooking process, it is known that it can modify the properties and the availability of nutrients in sofrito; however, there is not enough information regarding this issue. For this reason, the aim of the present work is to evaluate the cooking effect on the antioxidant capacity of different variants of tomato-based sofrito combined with other spices, through the analysis of total phenols content (TPC) and to evaluate the antioxidant capacity by using the method of free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH). Based on the results obtained, it can be confirmed that the basic sofrito composed of tomato, onion, garlic and olive oil and the sofrito with 1 g of rosemary added, are the ones with the highest content of phenols presenting greater antioxidant power than other industrial sofrito, and that of other variables of sofrito with added thyme or higher amounts of garlic. Moreover, it has been observed that in the elaboration of the tomato-based sofrito, it is possible to cook until 60 minutes, since the cooking process increases the bioavailability of the carotenoids when breaking the cell walls, which weakens the binding forces between the carotenoids and increases the levels of antioxidants present, confirmed both with the TPC and DPPH methods. It can be concluded that the cooking process of different variants of tomato-based sofrito, including spices, can improve the antioxidant capacity. The synergistic effects of different antioxidants may have a greater protective effect; increasing, also, the digestibility of proteins. In addition, the antioxidants help to deactivate the free radicals of diseases such as atherosclerosis, aging, immune suppression, cancer, and diabetes.

Keywords: antioxidants, cooking process, phenols sofrito

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Authors: L. Beltran, S. Solano, T. Lacruz, M. Blanco, M. Rojo, M. Graell, A. R. Sepulveda

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Introduction and Objective: Given the difficulties of assessing Binge Eating Disorder during childhood, episodes of Loss of Control (LOC) eating can be a key symptom. The objective is to know the prevalence of food psychopathology depending on the type of evaluation and find out which psychological characteristics differentiate overweight or obese children who present LOC from those who do not. Material and Methods: 170 children from 8 to 12 years of age with overweight or obesity (P > 85) were evaluated through the Primary Care Centers of Madrid. Sociodemographic data and psychological measures were collected through the Kiddie-Schedule for Affective Disorders & Schizophrenia, Present & Lifetime Version (K-SADS-PL) diagnostic interview and self-applied questionnaires: Children's eating attitudes (ChEAT), depressive symptomatology (CDI), anxiety (STAIC), general self-esteem (LAWSEQ), body self-esteem (BES), perceived teasing (POTS) and perfectionism (CAPS). Results: 15.2% of the sample exceeded the ChEAT cut-off point, presenting a risk of pathological eating; 5.88% presented an Eating Disorder through the diagnostic interview (2.35% Binge Eating disorder), and 33.53% had LOC episodes. No relationship was found between the presence of LOC and clinical diagnosis of eating disorders according to DSM-V; however, the group with LOC presented a higher risk of eating psychopathology using the ChEAT (p < .02). Significant differences were found in the group with LOC (p < .02): higher z-BMI, lower body self-esteem, greater anxious symptomatology, greater frequency of teasing towards weight, and greater effect of teasing both towards weight and competitions; compared to their peers without LOC. Conclusion: According to previous studies in samples with overweight children, in this Spanish sample of children with obesity, we found a prevalence of moderate eating disorder and a high presence of LOC episodes, which is related to both eating and general psychopathology. These findings confirm that the exclusion of LOC episodes as a diagnostic criterion can underestimate the presence of eating psychopathology during this developmental stage. According to these results, it is highly recommended to promote school context programs that approach LOC episodes in order to reduce associated symptoms. This study is included in a Project funded by the Ministry of Innovation and Science (PSI2011-23127).

Keywords: childhood obesity, eating psychopathology, loss-of-control eating, psychological symptomatology

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Authors: Carolina Beltran, Carlos De Los Reyes

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Acquired brain injury (ABI) is any physical and functional injury secondary to events that affect the brain tissue. It is one of the biggest causes of disability in the world and it has a high annual incidence in the pediatric population. There are several causes of ABI such as traumatic brain injury, central nervous system infection, stroke, hypoxia, tumors and others. The consequences can be cognitive, behavioral, emotional and functional. The cognitive rehabilitation is necessary to achieve the best outcomes for pediatric people with ABI. Cognitive orientation to daily occupational performance (CO-OP) is an individualized client-centered, performance-based, problem-solving approach that focuses on the strategy used to support the acquisition of three client-chosen goals. It has demonstrated improvements in the pediatric population with other neurological disorder but not in Spanish speakers with ABI. Aim: The main objective of this study was to determine the efficacy of cognitive orientation to daily occupational performances (CO-OP) adapted to Spanish speakers, in the level of independence and behavior in a pediatric population with ABI. Methods: Case studies with measure pre/post-treatment were used in three children with ABI, sustained at least before 6 months assessment, in school, aged 8 to 16 years, age ABI after 6 years old and above average intellectual ability. Twelve sessions of CO-OP adapted to Spanish speakers were used and videotaped. The outcomes were based on cognitive, behavior and functional independence measurements such as Child Behavior Checklist (CBCL), Behavior Rating Inventory of Executive Function (BRIEF), The Vineland Adaptive Behavior Scales (VINELAND, Social Support Scale (MOS-SSS) and others neuropsychological measures. This study was approved by the ethics committee of Universidad del Norte in Colombia. Informed parental written consent was obtained for all participants. Results: children were able to identify three goals and use the global strategy ‘goal-plan-do-check’ during each session. Verbal self-instruction was used by all children. CO-OP showed a clinically significant improvement in goals regarding with independence level and behavior according to parents and teachers. Conclusion: The results indicated that CO-OP and the use of a global strategy such as ‘goal-plan-do-check’ can be used in children with ABI in order to improve their specific goals. This is a preliminary version of a big study carrying in Colombia as part of the experimental design.

Keywords: cognitive rehabilitation, acquired brain injury, pediatric population, cognitive orientation to daily occupational performance

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

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