Autotrophic denitrification of nitrate rich wastewater in fluidized bed reactors using pyrite and elemental sulfur as electron donors

Maria Federica Carboni Simon Mills SONIA LORENA ARRIAGA GARCIA Gavin Collins Umer Zeeshan Ijaz Piet Nicolaas Luc Lens (2022, [Artículo])

"This study compared denitrification performances and microbial communities in fluidized bed reactors (FBRs) carrying out autotrophic denitrification using elemental sulfur (S0) and pyrite (FeS2) as electron donors. The reactors were operated for 220 days with nitrate loading rates varying between 23 and 200 mg N-NO-3 /Lmiddotd and HRT between 48 and 4 h. The highest denitrification rates achieved were 142.2 and 184.4 mg NNO-3 /Lmiddotd in pyrite and sulfur FBRs, respectively. Pyrite-driven denitrification produced less SO2- 4 and no buffer addition was needed to regulate the pH. The sulfur FBR needed instead CaCO3 to maintain the pH neutral and consequentially more sludge was produced (CaSO4 precipitation). The active community of pyrite-based systems was investigated and Azospira sp., Ferruginibacter sp., Rhodococcus sp. and Pseudomonas sp. were the predominant genera, while Thiobacillus sp. and Sulfurovum sp. dominated the active community in the sulfur FBR. However, Thiobacillus sp. became more dominant when operating at elevated nitrogen loading rate. Patterns of diversity and microbial community assembly were assessed and revealed three distinct stages of microbial community succession which corresponded with the operation of a period of high influent nitrate concentration (135 mg N-NO-3 /L). It is proposed that a high degree of functional redundancy in the initial microbial communities may have helped both reactors to respond better to such high influent nitrate concentration."

Pyrite Elemental sulfur Fluidized bed rector Nitrogen removal 16S rRNA Community assembly CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Effect of antimicrobial nanocomposites on Vibrio cholerae lifestyles: Pellicle biofilm, planktonic and surface-attached biofilm

ANAID MEZA VILLEZCAS (2019, [Artículo])

Vibrio cholerae is an important human pathogen causing intestinal disease with a high incidence in developing countries. V. cholerae can switch between planktonic and biofilm lifestyles. Biofilm formation is determinant for transmission, virulence and antibiotic resistance. Due to the enhanced antibiotic resistance observed by bacterial pathogens, antimicrobial nanomaterials have been used to combat infections by stopping bacterial growth and preventing biofilm formation. In this study, the effect of the nanocomposites zeolite-embedded silver (Ag), copper (Cu), or zinc (Zn) nanoparticles (NPs) was evaluated in V. cholerae planktonic cells, and in two biofilm states: pellicle biofilm (PB), formed between air-liquid interphase, and surface-attached biofilm (SB), formed at solid-liquid interfaces. Each nanocomposite type had a distinctive antimicrobial effect altering each V. cholerae lifestyles differently. The ZEO-AgNPs nanocomposite inhibited PB formation at 4 μg/ml, and prevented SB formation and eliminated planktonic cells at 8 μg/ml. In contrast, the nanocomposites ZEO-CuNPs and ZEO-ZnNPs affect V. cholerae viability but did not completely avoid bacterial growth. At transcriptional level, depending on the nanoparticles and biofilm type, nanocomposites modified the relative expression of the vpsL, rbmA and bap1, genes involved in biofilm formation. Furthermore, the relative abundance of the outer membrane proteins OmpT, OmpU, OmpA and OmpW also differs among treatments in PB and SB. This work provides a basis for further study of the nanomaterials effect at structural, genetic and proteomic levels to understand the response mechanisms of V. cholerae against metallic nanoparticles. © 2019 Meza-Villezcas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

bacterial protein, copper nanoparticle, nanocomposite, OmpT protein, OmpU protein, OmpW protein, outer membrane protein A, silver nanoparticle, unclassified drug, zeolite, zinc nanoparticle, antiinfective agent, copper, metal nanoparticle, nanocompos BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA MICROBIOLOGÍA MICROBIOLOGÍA

Reconocimiento continuo de la Lengua de Señas Mexicana

Continuous recognition of Mexican Sign Language

Ricardo Fernando Morfín Chávez (2023, [Tesis de maestría])

La Lengua de Señas Mexicana (LSM) es la lengua utilizada por la comunidad Sorda en México, y, a menudo, subestimada y pasada por alto por la comunidad oyente, lo que resulta en la exclusión sistemática de las personas Sordas en diversos aspectos de la vida. Sin embargo, la tecnología puede desempeñar un papel fundamental en acercar a la comunidad Sorda con la comunidad oyente, promoviendo una mayor inclusión y comprensión entre ambas. El objetivo principal de este trabajo es diseñar, implementar y evaluar un sistema de reconocimiento continuo de señas estáticas en LSM mediante, visión por computadora y técnicas de aprendizaje máquina. Se establecieron objetivos específicos, que incluyen la generación de un conjunto de datos de señas estáticas, pertenecientes al alfabeto manual de la LSM, el diseño de un modelo de reconocimiento, y la evaluación del sistema, tanto en la modalidad aislada como en la continua. La metodología involucra dos evaluaciones distintas. La primera se enfoca en el reconocimiento de señas estáticas en el dominio aislado, para ello se capturaron datos de 20 participantes realizando movimientos de la mano en múltiples ángulos. Se evaluaron diversas técnicas de aprendizaje automático, destacando que el enfoque basado en Máquinas de Soporte Vectorial (SVM) obtuvo los mejores resultados (F1-Score promedio del 0.91). La segunda evaluación se concentra en el reconocimiento continuo de señas estáticas, con datos recopilados de seis participantes con diferentes niveles de competencia en LSM, logrando un rendimiento sólido con errores cercanos al 7 %. Además, se evaluó la viabilidad del sistema en aplicaciones de tiempo real, demostrando un excelente desempeño (velocidad promedio de procesamiento de 45 cuadros por segundo). A pesar de los logros alcanzados, es importante reconocer que este proyecto se centró en el reconocimiento continuo de señas estáticas en LSM. Queda pendiente, como un desafío interesante, la exploración del reconocimiento continuo de señas dinámicas en LSM para futuras investigaciones. Se considera esencial explorar enfoques orientados a la escalabilidad y aplicaciones en tiempo real en investigaciones posteriores.

This study focuses on the continuous recognition of static signs in Mexican Sign Language (Lengua de Señas Mexicana (LSM)), the language used by the Deaf community in Mexico. Despite its significance, LSM is often underestimated and overlooked, leading to the systematic exclusion of Deaf individuals in various aspects of life. The primary objective of this work is to design, implement, and evaluate a continuous static sign recognition system in LSM using computer vision and machine learning techniques. Specific goals were established, including the creation of a dataset of static signs belonging to the manual alphabet of LSM, the design of a recognition model, and the evaluation of the system in both isolated and continuous modes. The methodology involves two distinct evaluations. The first one focuses on the recognition of static signs in the isolated domain, for which data from 20 participants performing hand movements at various angles were collected. Various machine learning techniques were evaluated, with the Máquinas de Soporte Vectorial (SVM)-based approach achieving the best results (average F1-Score of 0.91). The second evaluation centers on the continuous recognition of static signs, using data collected from six participants with varying levels of competence in LSM, achieving robust performance with errors close to 7 %. Furthermore, the feasibility of the system in real-time applications was assessed, demonstrating excellent performance (average processing speed of 45 frames per second). Despite the achievements, it is important to recognize that this project focused on continuous recognition of static signs in LSM. It remains an interesting challenge to explore the continuous recognition of dynamic signs in LSM for future research. It is considered essential to explore scalability-oriented approaches and real-time applications in subsequent investigations.

Lengua de Señas Mexicana (LSM), visión por computadora, aprendizaje automático, alfabeto manual de la LSM, reconocimiento automático de señas estáticas, reconocimiento aislado de señas, reconocimiento continuo de señas, aplicacion Mexican Sign Language (LSM), computer vision, machine learning, LSM manual alpahbet, automatic recognition of static signs, isolated sign recognition, continuous sign recognition, real-time aplications INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ORDENADORES ENSEÑANZA CON AYUDA DE ORDENADOR ENSEÑANZA CON AYUDA DE ORDENADOR