Search results for: Esteban Centeno

Authors: Adrián Esteban Ortiz-Valera, Angélica Lozano

Abstract:

In developing countries, informal trade is relevant, but it has been little studied in urban freight transport (UFT) context, although it is a challenge due to the non- contemplated demand it produces and the operational limitations it imposes. Hence, UFT operational improvements (initiatives) and freight attraction models must consider informal trade for developing countries. Afour phasesapproach for characterizing the commercial areas in developing countries (considering both formal and informal establishments) is proposed and applied to ten areas in Mexico City. This characterization is required to calculate real freight trip attraction and then select and/or adapt suitable initiatives. Phase 1 aims the delimitation of the study area. The following information is obtained for each establishment of a potential area: location or geographic coordinates, industrial sector, industrial subsector, and number of employees. Phase 2 characterizes the study area and proposes a set of indicators. This allows a broad view of the operations and constraints of UFT in the study area. Phase 3 classifies the study area according to seven indicators. Each indicator represents a level of conflict in the area due to the presence of formal (registered) and informal establishments on the sidewalks and streets, affecting urban freight transport (and other activities). Phase 4 determines preliminary initiatives which could be implemented in the study area to improve the operation of UFT. The indicators and initiatives relation allows a preliminary initiatives selection. This relation requires to know the following: a) the problems in the area (congested streets, lack of parking space for freight vehicles, etc.); b) the factors which limit initiatives due to informal establishments (reduced streets for freight vehicles; mobility and parking inability during a period, among others), c) the problems in the area due to its physical characteristics; and d) the factors which limit initiatives due to regulations of the area. Several differences in the study areas were observed. As the indicators increases, the areas tend to be less ordered, and the limitations for the initiatives become higher, causing a smaller number of susceptible initiatives. In ordered areas (similar to the commercial areas of developed countries), the current techniquesfor estimating freight trip attraction (FTA) can bedirectly applied, however, in the areas where the level of order is lower due to the presence of informal trade, this is not recommended because the real FTA would not be estimated. Therefore, a technique, which consider the characteristics of the areas in developing countries to obtain data and to estimate FTA, is required. This estimation can be the base for proposing feasible initiatives to such zones. The proposed approach provides a wide view of the needs of the commercial areas of developing countries. The knowledge of these needs would allow UFT´s operation to be improved and its negative impacts to be minimized.

Keywords: freight initiatives, freight trip attraction, informal trade, urban freight transport

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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: Esteban Maioli

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Generational studies have an important research tradition in social and human sciences. On the one hand, the speed of social change in the context of globalization imposes the need to research the transformations are identified both the subjectivity of the agents involved and its inclusion in the institutional matrix, specifically employment. Generation Z, (generally considered as the population group whose birth occurs after 1995) have unique psycho-social characteristics. Gen Z is characterized by a different set of values, beliefs, attitudes and ambitions that impact in their concrete action in organizational structures. On the other hand, managers often have to deal with generational differences in the workplace. Organizations have members who belong to different generations; they had never before faced the challenge of having such a diverse group of members. The members of each historical generation are characterized by a different set of values, beliefs, attitudes and ambitions that are manifest in their concrete action in organizational structures. Gen Z it’s the only one who can fully be considered "global," while its members were born in the consolidated context of globalization. Some salient features of the Generation Z can be summarized as follows. They’re the first fully born into a digital world. Social networks and technology are integrated into their lives. They are concerned about the challenges of the modern world (poverty, inequality, climate change, among others). They are self-expressive, more liberal and open to change. They often bore easily, with short attention spans. They do not like routine tasks. They want to achieve a good life-work balance, and they are interested in a flexible work environment, as opposed to traditional work schedule. They are critical thinkers, who come with innovative and creative ideas to help. Research design considered methodological triangulation. Data was collected with two techniques: a self-administered survey with multiple choice questions and attitudinal scales applied over a non-probabilistic sample by reasoned decision. According to the multi-method strategy, also it was conducted in-depth interviews. Organizations constantly face new challenges. One of the biggest ones is to learn to manage a multi-generational scope of work. While Gen Z has not yet been fully incorporated (expected to do so in five years or so), many organizations have already begun to implement a series of changes in its recruitment and development. The main obstacle to retaining young talent is the gap between the expectations of iGen applicants and what companies offer. Members of the iGen expect not only a good salary and job stability but also a clear career plan. Generation Z needs to have immediate feedback on their tasks. However, many organizations have yet to improve both motivation and monitoring practices. It is essential for companies to take a review of organizational practices anchored in the culture of the organization.

Keywords: employment, expectations, generation Z, organizational culture, organizations, psycho-social characteristics

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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: Mei Chen, Yingping Hou, Michelle Hao, Soheil Aghamohammadzadeh, Esteban Terzo, Roger Clark, Vijay Modur

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Background: Recessive Dystrophic Epidermolysis Bullosa (RDEB) is a genetic skin condition characterized by skin tearing and unremitting blistering upon minimal trauma. Repeated blistering, fibrosis, and scarring lead to aggressive squamous cell carcinoma later in life. RDEB is caused by mutations in the COL7A1 gene encoding collagen type VII (C7), the major component of anchoring fibrils mediating epidermis-dermis adherence. Nonsense mutations in the COL7A1 gene of a subset of RDEB patients leads to premature termination codons (PTC). Similarly, most Junctional Epidermolysis Bullosa (JEB) cases are caused by nonsense mutations in the LAMB3 gene encoding the β3 subunit of laminin 332. Currently, there is an unmet need for the treatment of RDEB and JEB. Zikani Therapeutics has discovered an array of macrocyclic compounds with ring structures similar to macrolide antibiotics that can facilitate readthrough activity of nonsense mutations in the COL7A1 and LAMB3 genes by acting as Ribosome Modulating Agents (RMAs). The medicinal chemistry synthetic advancements of these macrocyclic compounds have allowed targeting the human ribosome while preserving the structural elements responsible for the safety and pharmacokinetic profile of clinically used macrolide antibiotics. Methods: C7 expression was used as a measure of readthrough activity by immunoblot assays in two primary human fibroblasts from RDEB patients (R578X/R578X and R163X/R1683X-COL7A1). Similarly, immunoblot assays in C325X/c.629-12T > A-LAMB3 keratinocytes were used to measure readthrough activity for JEB. The relative readthrough activity of each compound was measured relative to Gentamicin. An imaging-based fibroblast migration assay was used as an assessment of C7 functionality in RDEB-fibroblasts over 16-20 hrs. The incubation period for the above experiments was 48 hrs for RDEB fibroblasts and 72 hours for JEB keratinocytes. Results: 9 RMAs demonstrated increased protein expression in both patient RDEB fibroblasts. The highest readthrough activity at tested concentrations without cytotoxicities increased protein expression up to 179% of Gentamicin (400 µg/ml), with favored readthrough activity in R163X/R1683X-COL7A1 fibroblasts. Concurrent with protein expression, fibroblast hypermotility phenotype observed in RDEB was rescued by reducing motility by ~35% to WT levels (the same level as 690 µM Gentamicin treated cells). Laminin β3 expression was also shown to be increased by 6 RMAs in keratinocytes to 33-83% of (400 µg/ml) Gentamicin. Conclusions: To date, 9 RMAs have been identified that enhance the expression of functional C7 in a mutation-dependent manner in two different RDEB patient fibroblast backgrounds (R578X/R578X and R163X/R1683X-COL7A1). A further 6 RMAs have been identified that enhance the readthrough of C325X-LAMB3 in JEB patient keratinocytes. Based on the clinical trial conducted by us with topical gentamycin in 2017, Zikani’s RMAs achieve clinically significant levels of read-through for the treatment of recessive dystrophic and Junctional Epidermolysis Bullosa.

Keywords: epidermolysis bullosa, nonsense mutation, readthrough, ribosome modulation

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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

Abstract:

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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Authors: Gail Schofield, Antoine M. Dujon, Nicole Esteban, Rebecca M. Lester, Graeme C. Hays

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Variation in diel movement patterns during migration provides information on the strategies used by animals to maximize energy efficiency and ensure the successful completion of migration. For instance, many flying and land-based terrestrial species stop to rest and refuel at regular intervals along the migratory route, or at transitory ‘stopover’ sites, depending on resource availability. However, in cases where stopping is not possible (such as over–or through deep–open oceans, or over deserts and mountains), non-stop travel is required, with animals needing to develop strategies to rest while actively traveling. Recent advances in biologging technologies have identified mid-flight micro sleeps by swifts in Africa during the 10-month non-breeding period, and the use of lateralized sleep behavior in orca and bottlenose dolphins during migration. Here, highly accurate locations obtained by Argos-linked Fastloc-GPS transmitters of adult green (n=8 turtles, 9487 locations) and loggerhead (n=46 turtles, 47,588 locations) sea turtles migrating around thousand kilometers (over several weeks) from breeding to foraging grounds across the Indian and Mediterranean oceans were used to identify potential resting strategies. Stopovers were only documented for seven turtles, lasting up to 6 days; thus, this strategy was not commonly used, possibly due to the lack of potential ‘shallow’ ( < 100 m seabed depth) sites along routes. However, observations of the day versus night speed of travel indicated that turtles might use other mechanisms to rest. For instance, turtles traveled an average 31% slower at night compared to day during oceanic crossings. Slower travel speeds at night might be explained by turtles swimming in a less direct line at night and/or deeper dives reducing their forward motion, as indicated through studies using Argos-linked transmitters and accelerometers. Furthermore, within the first 24 h of entering waters shallower than 100 m towards the end of migration (the depth at which sea turtles can swim and rest on the seabed), some individuals travelled 72% slower at night, repeating this behavior intermittently (each time for a one-night duration at 3–6-day intervals) until reaching the foraging grounds. If the turtles were, in fact, resting on the seabed at this point, they could be inactive for up to 8-hours, facilitating protracted periods of rest after several weeks of constant swimming. Turtles might not rest every night once within these shallower depths, due to the time constraints of reaching foraging grounds and restoring depleted energetic reserves (as sea turtles are capital breeders, they tend not to feed for several months during migration to and from the breeding grounds and while breeding). In conclusion, access to data-rich, highly accurate Argos-linked Fastloc-GPS provided information about differences in the day versus night activity at different stages of migration, allowing us, for the first time, to compare the strategies used by a marine vertebrate with terrestrial land-based and flying species. However, the question of what resting strategies are used by individuals that remain in oceanic waters to forage, with combinations of highly accurate Argos-linked Fastloc-GPS transmitters and accelerometry or time-depth recorders being required for sufficient numbers of individuals.

Keywords: argos-linked fastloc GPS, data loggers, migration, resting strategy, telemetry

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