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La anosmia es uno de los síntomas frecuentes identificados como parte del cuadro clínico de la infección por SARS-CoV2, sin embargo, los mecanismos por los cuales ocurre la disfunción olfatoria no están completamente esclarecidos. Previamente los coronavirus fueron identificados como una familia de virus que pueden causar anosmia, pero en el COVID-19, a diferencia de la familia de los rinovirus, esta no se relaciona con un proceso inflamatorio de la mucosa nasal asociado a obstrucción nasal o rinorrea, lo cual sugiere que la anosmia podría deberse a disturbios en la conducción nerviosa del estímulo olfatorio. La anosmia de los pacientes con COVID-19 recuerda en algunos aspectos al síndrome de Kallman, el cual también cursa con anosmia, su etiología es genética y se debe a mutaciones en el gen ANOS 1 que codifica la molécula de adhesión celular anosmia. En esta revisión se suministrará una recopilación de evidencias fisiopatológicas e interacciones moleculares mediante la cual la proteína anosmia podría modificar el curso de la infección por SARS-CoV2, para ello, se realizó una búsqueda computarizada de literatura para desvendar las posibles relaciones que existen en entre la anosmia y las infecciones causadas por el SARS-CoV2.

The dissemination of antimicrobial-resistant bacteria in environmental waters is an emerging concern in medical and industrial settings. In the present study, our research team analyzed superficial water samples from 3 different collection sites along the Ozama River, the most important river in the Dominican metropolitan area. Seventy-six isolates were obtained from culture media previously enriched with cefotaxime and imipenem and subsequently identified by MALDI-ToF. Our isolates spanned 12 genera of bacteria; over 30% were of clinical relevance, and 43% exhibited a phenotype classified as multi-drug resistance. The most frequent species identified as Stenotrophomonas maltophilia (n = 33), an emerging nosocomial pathogen. This study constitutes part of the initiative to understand the profiles of the perils of multi-drug resistance in metropolitan areas of the Dominican Republic: a nation with poor antibiotic use regulation.IMPORTANCENearly two billion people are sourcing their water from low-quality reservoirs world-wide. These reservoirs consist of contaminated waters with anthropogenic pollutants such as antibiotics, disinfectants, and other substances used to treat water in societies with scarce resources and unregulated industries. Furthermore, the exposure of these antibiotics to potable water reservoirs promotes the increase of clinically relevant bacteria with antibiotic-resistance capabilities, becoming a public health crisis. Therefore, treating patients with infectious diseases and providing prophylactic measures against infection-associated conditions (such as post-surgical recovery) has become progressively more difficult. Based on this evidence frame, it is of considerable importance to analyze the bacterial profiles of rivers that fall prey to anthropogenic contamination, as these investigations on antibiotic resistance will, of course, benefit the life of humans.

Our research team compared the performance of matrix-assisted laser desorption/ionization followed by a time of flight (MALDI-TOF) mass spectrometry and genomic DNA extraction followed by sequencing, assembly and alignment for phylogenetic assessment. We performed these comparisons to determine our methodology’s overall efficacy and accuracy for environmental bacteria. In addition, we collected samples from various contaminated rivers in the Dominican Republic. For both methods, we analyzed these results and reported the main differences between each method.

Después de su descubrimiento en China, en diciembre de 2019, el virus SARS-CoV-2 causante de la enfermedad COVID-19, comenzó a propagarse por la mayoría de los países del mundo, provocando una cantidad importante de infectados y muertos, situación que ha generado una gran presión en los sistemas de salud y gobiernos de los diferentes países, que en muchos casos no contaban con los recursos e infraestructuras para enfrentar una pandemia provocada por el virus, como fue el caso de República Dominicana. Usando los datos públicos del avance de la pandemia en la Republica Dominicana, desde el 1 de marzo hasta el 30 de abril, se modeló y pronosticó la posible evolución del COVID-19 en este país, utilizando de tres modelos matemáticos SIR, SEIR-Extendido y log-lineal. Estos modelos permitieron proyectar los diferentes escenarios de contagios y evolución de la enfermedad, además de pronosticar el pico y el descenso de la curva de casos. Según los resultados obtenidos, el país se encontraba en un proceso de crecimiento exponencial de los contagios; el pico de los contagios fue pronosticado ocurriría entre la segunda mitad del mes de mayo y la primera mitad del mes de junio, con un estimado de infectados entre 100,000 y 200,000 individuos. Los resultados obtenidos en este estudio permiten obtener una idea de cuál podría ser el impacto humano y social del COVID-19 en la República Dominicana.

One of the biggest problems that the Dominican Republic has had in recent decades is the efficient management of solid waste produced by the population. This problem has worsened in recent years due to the decrease in available areas for the construction of sanitary landfills, the lack of recycling culture in the population, the deficiency in waste collection, and the scarce legal controls aimed at preserving water and air and soil, among other factors. The objective of this study was to explore the management of solid waste by the population and the municipality of Puñal, province of Santiago, to evaluate and analyze the situation and generation of solid waste, municipal solid waste management services, and the attitudes of the population regarding recycling projects and waste management, to evaluate the viability and sustainability of the use organic waste for energy generation. A total of 275 households from 29 localities in the municipality of Puñal were surveyed, which allowed for a significant population sample. According to the results obtained, the most significant type of waste produced by families is organic waste, followed by plastic waste and paper. Of the total organic waste produced in the municipality, 53% of solid waste is disregarded through the municipal waste collection system, while 47% is used as plant fertilizers or animal feed. On the other hand, most households receive the municipal waste collection service and pay for this service. The results of our research show that the implementation of an energy production system based on organic waste would be viable in the municipality of Puñal. However, a more efficient waste collection system would be necessary and the development of programs and projects that allow all households to participate in the system.

La bioinformática es un área que ha modificado la forma en que se diseñan y se desarrollan los experimentos e investigaciones de las áreas biológicas. La biotecnología no ha quedado fuera de los alcances de la bioinformática, impactando directamente áreas como el descubrimiento y el desarrollo de fármacos, mejoramiento de cultivos, biorremediación, estudios de la diversidad ambiental, patología molecular, entre otras. Esto se debe, en gran medida, al desarrollo de las tecnologías de secuenciación de alto rendimiento o Next-generation sequencing (NGS), que han generado gran cantidad de datos que deben ser procesados y analizados para producir nuevos conocimientos y descubrimientos. Lo anterior ha promovido que dos áreas de la bioinformática y la ciencia de la computación, machine learning y deep learning, hayan sido utilizadas para el análisis de estos datos. El “aprendizaje de máquina” aplica técnicas que permiten que las computadoras aprendan, mientras que el “aprendizaje profundo” genera modelos de redes neuronales artificiales que intenta imitar el funcionamiento del cerebro humano, permitiéndoles aprender a partir de los datos y mejorar su aprendizaje a través de las experiencias. Estas dos áreas son esenciales para poder identificar, analizar, interpretar y obtener conocimientos de la gran cantidad de datos biológicos (Big biological data). En este trabajo hacemos una revisión de estas dos áreas: el aprendizaje de máquina y el aprendizaje profundo, orientado al impacto y sus aplicaciones en el área de biotecnología.

Simple Summary Here, we compared the performance of four different autoencoders: (a) vanilla, (b) sparse, (c) denoising, and (d) variational for subtype detection on four cancer types: Glioblastoma multiforme, Colon Adenocarcinoma, Kidney renal clear cell carcinoma, and Breast invasive carcinoma. Multiview dataset comprising gene expression, DNA methylation, and miRNA expression from TCGA is fed into an autoencoder to get a compressed nonlinear representation. Then the clustering technique was applied on that compressed representation to reveal the subtype of cancer. Though different autoencoders’ performance varies on different datasets, they performed much better than standard data fusion techniques such as PCA, kernel PCA, and sparse PCA. Abstract A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score and found that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we compared the effect of feature selection and similarity measures for subtype detection. For further evaluation, we used the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes. The results obtained are consistent with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

A heterogeneous disease like cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), patients' survival vary significantly and show different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed, such as kernel-based fusion, matrix factorization, and deep learning autoencoders.

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-Seq are one of the most commonly used techniques to perform transcriptional experiments. Computational analysis of such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-Seq datasets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsigand next identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50% of the genes identified as influential had some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes had crucial roles or participated in processes associated with the response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-seq is one of the most commonly used techniques to perform transcriptional experiments. Computational analysis on such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-seq data-sets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsig and identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50\% of the genes identified as influential have some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes have crucial roles or participate in processes associated with response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

Housekeeping genes (HKGs), are essential for gene expression based studies performed through Reverse Transcriptase-polymerase Chain Reaction (RT-qPCR). These genes are related with the basic cellular processes that are essential for cell maintenance, survival and function. Thus, HKGs should be expressed in all cells of an organism regardless of the tissue type, cell state or cell condition. High-throughput technologies, including RNA sequencing (RNA-seq), are used to study and identify these types of genes. RNA-seq is a high-throughput method that allows the measurement of gene expression profiles in a target tissue or an isolated cell. Moreover, machine learning methods are routinely applied in different genomics related areas to enable the interpretation of large datasets, including those related to gene expression. This study reports a new machine learning based approach to identify candidate HKGs in silico from RNA-seq gene expression data. The approach enabled the identification of stable HKGs candidates in RNA-seq data from Corynebacterium pseudotuberculosis. These genes showed stable expression under different stress conditions as well as low variation index and fold changes. Furthermore, some of these genes were already reported in the literature as HKGs or HKGs candidates for the same or other bacterial organisms, which reinforced the accuracy of the proposed method. We present a novel approach based on K-means algorithm, internal metrics and machine learning methods that can identify stable housekeeping genes from gene expression data with high accuracy and efficiency.

One of the biggest problems that the Dominican Republic has had in recent decades is the efficient management of solid domestic waste. This problem has worsened in recent years due to the decrease in available areas for the construction of sanitary landfills, the lack of recycling culture in the population, the deficiency in waste collection, and the scarce legal controls aimed at preserving water, air and soil among other factors. The objective of this study is to explore the management of solid waste by the population and the municipality of Puñal, province of Santiago, to evaluate and analyze the situation and generation of solid waste, municipal solid waste management services, and the attitudes of the population regarding recycling projects and waste management. A total of 275 households from 29 localities in the municipality of Puñal were surveyed, which allowed for a significant population sample. According to the results, the most significant type of waste produced by families is organic waste, followed by plastic waste and paper. Of the total organic waste produced in the municipality, 53% of solids wastes are handled through the municipal waste collection system, while 47% is used as plant fertilizers or animal feed. On the other hand, most households receive the municipal waste collection service and pay for this service, through which the municipal government collects, processes, and deposits the waste in different landfills. However, a more efficient waste collection system and the development of programs and projects that allow households to manage the solid waste efficiently they produce would be necessary.

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most common neurodegenerative disorders related to aging. Though several risk factors are shared between these two diseases, the exact relationship between them is still unknown. In this paper, we analyzed how these two diseases relate to each other from the genomic, epigenomic, and transcriptomic viewpoints. Using an extensive literature mining, we first accumulated the list of genes from major genome-wide association (GWAS) studies. Based on these GWAS studies, we observed that only one gene (HLA-DRB5) was shared between AD and PD. A subsequent literature search identified a few other genes involved in these two diseases, among which SIRT1 seemed to be the most prominent one. While we listed all the miRNAs that have been previously reported for AD and PD separately, we found only 15 different miRNAs that were reported in both diseases. In order to get better insights, we predicted the gene co-expression network for both AD and PD using network analysis algorithms applied to two GEO datasets. The network analysis revealed six clusters of genes related to AD and four clusters of genes related to PD; however, there was very low functional similarity between these clusters, pointing to insignificant similarity between AD and PD even at the level of affected biological processes. Finally, we postulated the putative epigenetic regulator modules that are common to AD and PD.

Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common neurodegenerative disorders related to aging. Though several risk factors are shared between these two diseases, the exact relationship between these two diseases is still unknown. In this paper, we analyzed how these diseases relate to each other from a genomics viewpoint. Using an extensive literature search, we accumulated the list of genes from the major genome-wide association (GWAS) studies. However, we found only one gene (HLA-DRB5) reported in these GWAS studies that are common between AD and PD. We also listed all the miRNAs that have been previously reported for AD and PD. Here we found 15 different miRNAs that were reported in both diseases. In order to get better insights, we predicted the gene coexpression network for both AD and PD. Network analysis on these networks show six clusters of genes related to AD and four clusters of genes related to PD.

This study developed a computational tool with a graphical interface and a web-service that allows the identification of phage regions through homology search and gene clustering. It uses G+C content variation evaluation and tRNA prediction sites as evidence to reinforce the presence of prophages in indeterminate regions. Also, it performs the functional characterization of the prophages regions through data integration of biological databases. The performance of PhageWeb was compared to other available tools (PHASTER, Prophinder, and PhiSpy) using Sensitivity (Sn) and Positive Predictive Value (PPV) tests. As a reference for the tests, more than 80 manually annotated genomes were used. In the PhageWeb analysis, the Sn index was 86.1% and the PPV was approximately 87%, while the second best tool presented Sn and PPV values of 83.3 and 86.5%, respectively. These numbers allowed us to observe a greater precision in the regions identified by PhageWeb while compared to other prediction tools submitted to the same tests. Additionally, PhageWeb was much faster than the other computational alternatives, decreasing the processing time to approximately one-ninth of the time required by the second best software. PhageWeb is freely available at http://computationalbiology.ufpa.br/phageweb.