Molecular and geochemical basis of microbially induced carbonate precipitation for treating acid mine drainage: The case of a novel Sporosarcina genomospecies from mine tailings.

Microbially induced carbonate precipitation (MICP) immobilizes toxic metals and reduces their bioavailability in aqueous systems. However, its application in the treatment of acid mine drainage (AMD) is poorly understood. In this study, the genomes of Sporosarcina sp. UB5 and UB10 were sequenced. Urease, carbonic anhydrases, and metal resistance genes were identified and enzymatic assays were performed for their validation. The geochemical mechanism of precipitation in AMD was elucidated through geo-mineralogical analysis. Sporosarcina sp. UB5 was shown to be a new genomospecies, with an average nucleotide identity < 95 % (ANI) and DNA-DNA hybridization < 70 % (DDH) whereas UB10 is close to S. pasteurii. UB5 contained two urease operons, whereas only one was identified in UB10. The ureolytic activities of UB5 and UB10 were 122.67 ± 15.74 and 131.70 ± 14.35 mM NH4+ min-1, respectively. Both strains feature several carbonic anhydrases of the α, ß, or γ families, which catalyzed the precipitation of CaCO3. Only Sporosarcina sp. UB5 was able to immobilize metals and neutralize AMD. Geo-mineralogical analyses revealed that UB5 directly immobilized Fe (1-23 %), Mn (0.65-1.33 %) and Zn (0.8-3 %) in AMD via MICP and indirectly through adsorption to calcite and binding to bacterial cell walls. The MICP-treated AMD exhibited high removal rates (>67 %) for Ag, Al, As, Ca, Cd, Co, Cu, Fe, Mn, Pb, and Zn, and a removal rate of 15 % for Mg. This study provides new insights into the MICP process and its applications to AMD treatment using autochthonous strains.

An Assessment of the Effects of Guanidinoacetic Acid on the Performance and Immune Response of Laying Hens Fed Diets with Three Levels of Metabolizable Energy.

Different levels of metabolizable energy (ME) and the inclusion of guanidinoacetic acid (GAA) in the diet of 53-week-old Lohmann LSL-CLASSIC hens were used to evaluate its effect on reproductive parameters, egg quality, intestinal morphology, and the immune response. Six diets were used in a 3 × 2 factorial design, with three levels of ME (2850, 2800, and 2750 kcal/kg), and with (0.08%) or without the inclusion of GAA. The addition of GAA to diets with low levels of ME increased (p < 0.05) egg production and egg mass. Moreover, hens fed with 2800 kcal/g without GAA had the highest concentration (p < 0.05) of serum interleukin IL-2, while those fed diets with the same amount of ME but supplemented with 0.08% GAA had the lowest concentration. Finally, the inclusion of 0.08% GAA increased (p < 0.05) the concentration of vascular endothelial growth factor (VEGF), regardless of the ME level in the diet. This study highlights the potential role of GAA in decreasing the energy level of ME (50-100 kcal/g) in the feeding of hens and in the modulation of specific immune responses. Further research is recommended to fully understand the mechanisms of action of GAA on the mechanism target of rapamycin and its relationship with the immune response.

Cultural adaptation and psychometric properties of the online learning climate scale for Chilean university students.

Introduction: The COVID-19 pandemic has profoundly changed university teaching and learning formats, leading to a significant increase in online learning. Consequently, the crisis has facilitated the potential development of this educational modality. However, researchers need adapted and validated instruments to assess the online learning climate in universities. Aim: This study aimed to adapt and psychometrically validate the Online Learning Climate Scale (OLCS) for Chilean university students. Method: Quantitative research was conducted with a non-experimental and cross-sectional, design executed in two phases: the first was oriented to the cultural adaptation of the instrument, and the second was focused on analyzing its psychometric properties in a sample of 491 university students. Results: A translated and culturally adapted version was obtained, composed of 15 items distributed in a factorial structure composed of four dimensions that showed excellent adjustment to the data [χ2 (84) = 189.628; p < 0.001; CFI = 0.979; TLI = 0.973; RMSEA = 0.051 (IC90% 0.044-0.059); SRMR = 0.028]; internal consistency was estimated through Cronbach's alpha and ranged between 0.892 and 0.955, and strict invariance between men and women was achieved. Discussion: The Online Learning Climate Scale (OLCS) is a valid and reliable measure for measuring the online learning climate within the Chilean higher education context so that it can be used both in research and in monitoring management programs in educational environments.

Effect of Dietary Guanidinoacetic Acid Levels on the Mitigation of Greenhouse Gas Production and the Rumen Fermentation Profile of Alfalfa-Based Diets.

The objective of this study was to evaluate the effect of different percentages of alfalfa (Medicago sativa L.) hay (AH) and doses of guanidinoacetic acid (GAA) in the diet on the mitigation of greenhouse gas production, the in vitro rumen fermentation profile and methane (CH4) conversion efficiency. AH percentages were defined for the diets of beef and dairy cattle, as well as under grazing conditions (10 (AH10), 25 (AH25) and 100% (AH100)), while the GAA doses were 0 (control), 0.0005, 0.0010, 0.0015, 0.0020, 0.0025 and 0.0030 g g-1 DM diet. With an increased dose of GAA, the total gas production (GP) and methane (CH4) increased (p = 0.0439) in the AH10 diet, while in AH25 diet, no effect was observed (p = 0.1311), and in AH100, GP and CH4 levels decreased (p = 0.0113). In addition, the increase in GAA decreased (p = 0.0042) the proportion of CH4 in the AH25 diet, with no influence (p = 0.1050) on CH4 in the AH10 and AH100 diet groups. Carbon monoxide production decreased (p = 0.0227) in the AH100 diet with most GAA doses, and the other diets did not show an effect (p = 0.0617) on carbon monoxide, while the production of hydrogen sulfide decreased (p = 0.0441) in the AH10 and AH100 diets with the addition of GAA, with no effect observed in association with the AH25 diet (p = 0.3162). The pH level increased (p < 0.0001) and dry matter degradation (DMD) decreased (p < 0.0001) when AH was increased from 10 to 25%, while 25 to 100% AH contents had the opposite effect. In addition, with an increased GAA dose, only the pH in the AH100 diet increased (p = 0.0142 and p = 0.0023) the DMD in the AH10 diet group. Similarly, GAA influenced (p = 0.0002) SCFA, ME and CH4 conversion efficiency but only in the AH10 diet group. In this diet group, it was observed that with an increased dose of GAA, SCFA and ME increased (p = 0.0002), while CH4 per unit of OM decreased (p = 0.0002) only with doses of 0.0010, 0.0015 and 0.0020 g, with no effect on CH4 per unit of SCFA and ME (p = 0.1790 and p = 0.1343). In conclusion, the positive effects of GAA depend on the percentage of AH, and diets with 25 and 100% AH showed very little improvement with the addition of GAA, while the diet with 10% AH presented the best results.

Effective matching of patients to clinical trials using entity extraction and neural re-ranking.

INTRODUCTION: Clinical trials (CTs) often fail due to inadequate patient recruitment. Finding eligible patients involves comparing the patient's information with the CT eligibility criteria. Automated patient matching offers the promise of improving the process, yet the main difficulties of CT retrieval lie in the semantic complexity of matching unstructured patient descriptions with semi-structured, multi-field CT documents and in capturing the meaning of negation coming from the eligibility criteria. OBJECTIVES: This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-trials paradigm. Our approach involves two key components in a pipeline-based model: (i) a data enrichment technique for enhancing both queries and documents during the first retrieval stage, and (ii) a novel re-ranking schema that uses a Transformer network in a setup adapted to this task by leveraging the structure of the CT documents. METHODS: We use named entity recognition and negation detection in both patient description and the eligibility section of CTs. We further classify patient descriptions and CT eligibility criteria into current, past, and family medical conditions. This extracted information is used to boost the importance of disease and drug mentions in both query and index for lexical retrieval. Furthermore, we propose a two-step training schema for the Transformer network used to re-rank the results from the lexical retrieval. The first step focuses on matching patient information with the descriptive sections of trials, while the second step aims to determine eligibility by matching patient information with the criteria section. RESULTS: Our findings indicate that the inclusion criteria section of the CT has a great influence on the relevance score in lexical models, and that the enrichment techniques for queries and documents improve the retrieval of relevant trials. The re-ranking strategy, based on our training schema, consistently enhances CT retrieval and shows improved performance by 15% in terms of precision at retrieving eligible trials. CONCLUSION: The results of our experiments suggest the benefit of making use of extracted entities. Moreover, our proposed re-ranking schema shows promising effectiveness compared to larger neural models, even with limited training data. These findings offer valuable insights for improving methods for retrieval of clinical documents.

Measuring school social climate in Latin America: the need for multidimensional and multi-informant tests - A systematic review.

Introduction: School social climate is central to understanding learning experiences in education environments. Previous studies describe various conceptual and operational definitions around the construct; however, there are no records of reviews focused on Latin America. Aim: This study sought to analyze the available evidence and the quality of school social climate measures in Latin America through a systematic review of the literature based on the PRISMA methodology and the COSMIN checklist to assess the psychometric properties of the instruments. Methodology: The Web of Science, Scopus, Psycinfo, and SciELO databases were consulted. A total of 582 records was identified, of which 27 fulfilled the inclusion criteria and methodological quality to be included in the systematization. Results: The results show that the country with the greatest scientific production on the topic is Chile, the measures are centered mainly on the students' perspective and the most used instrument is the CECSCE. In addition, a common aspect to all the records is that they were not sufficient to capture the complexity of school social climate. Conclusion: Multidimensional and multi-informant measures are needed to adequately assess the construct.

A postmortem study of patients vaccinated for SARS-CoV-2 in Colombia.

INTRODUCTION AND OBJECTIVE: A new coronavirus produces a disease designated as coronavirus disease 2019 (COVID-19). Vaccination against COVID-19 has resulted in decreased mortality. Postmortems of vaccinated patients play an important part in the forensic analysis of adverse effects after vaccination, which is essential for determining its efficacy and security. The main objective of this study was to describe the results of autopsies of patients vaccinated for SARS-CoV-2 carried out in two major centers in Colombia. MATERIALS AND METHODS: A descriptive cross-sectional study of 121 autopsies was performed following Colombian regulations in two main hospitals in Bogotá, Colombia, between March 1st and April 31st, 2021. RESULTS: 118 of the 121 patients (97.52%) had been vaccinated with CoronaVac (Sinovac); only 3 had received other vaccines. Sudden cardiac death was the leading cause of death, with pulmonary embolism another critical finding. No relation between the cause of death and vaccination against SARS-CoV-2 was found. CONCLUSIONS: A clinical autopsy is a vital for an accurate post-mortem diagnosis. Any relation between the SARS-CoV-2 vaccine and the cause of death should be carefully studied in order to provide the general public with evidence-based information about the safety of the vaccination.

Environmental, Economic, and Social Aspects of Human Urine Valorization through Microbial Fuel Cells from the Circular Economy Perspective.

Population growth increases the challenge of meeting basic human needs, such as water, a limited resource. Consumption habits and water pollution have compromised natural resources to unsustainable levels. Sustainable effluent treatment practices, such as decentralized systems focused on energy, nutrients, and water recovery, have attracted the attention of the scientific community. Human urine (HU) is a physiological liquid waste whose main component is water (~95%). HU has a significant amount of nutrients, such as N, P, K, and organic matter, which are usually lacking in fecal coliforms. Therefore, the possibility exists of recovering nutrients and energy from HU using sustainable and non-sustainable technologies. Treating HU in bioelectrochemical systems (BES) is a novel alternative to obtaining byproducts from this effluent more sustainably than in electrochemical systems. Microbial fuel cells (MFCs) are an interesting example, contributing to HU revalorization from unwanted waste into a valuable resource of nutrients, energy, and water. Even when urine-operated MFCs have not generated attractive potential outputs or produced considerable amounts of bioelectricity, this review emphasizes HU advantages as nutrients or water sources. The aim of this review was to analyze the current development of BES for HU treatment based on the water circular economy, discussing challenges and perspectives researchers might encounter.

Genotoxic Biomonitoring in Children Living near the El Fraile Mine Tailings in Northern Guerrero State, Mexico.

A genotoxic study was conducted with 101 elementary school children (56 girls and 45 boys) in the 6-7, 8-9, and 10-12 age ranges from El Fraile rural community, which is located beside the El Fraile mine tailings in Taxco of Alarcon City, in northern Guerrero State, Mexico. For this, we used the alkaline comet assay in exfoliated buccal mucosa cells, scoring three genotoxic parameters: tail intensity, tail moment, and tail length. Additionally, we detected oxidative DNA damage through urinary 8-OHdG levels by enzyme-linked immunosorbent assay. We also evaluated a control group consisting of 101 children in the same age ranges from Chilpancingo City, Guerrero, who had never lived near mining zones. Genotoxic results showed that there was a significant increase in three genotoxic parameters and urinary 8-OHdG levels in the exposed children group compared with the control group. Analysis of MANOVA revealed that boys aged 8 and 9 years had higher DNA damage than girls from the same exposure group, and Spearman's analysis identified a positive correlation between DNA damage and sex and age. This study provides the first valuable genotoxic data in children living in areas with environmental pollution.

Deep learning representations to support COVID-19 diagnosis on CT slices. / Representaciones basadas en aprendizaje profundo como apoyo del diagnóstico de la COVID-19 en cortes de tomografía computarizada

INTRODUCTION: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist's expertise, which may result in subjective evaluations. OBJECTIVE: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. MATERIALS AND METHODS: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic's dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. RESULTS: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. CONCLUSION: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.

Introducción. La enfermedad por coronavirus (COVID-19) es actualmente el principal problema de salud pública en el mundo. En este contexto, el análisis automático de tomografías computarizadas (TC) surge como una herramienta diagnóstica complementaria que permite caracterizar hallazgos radiológicos, y categorizar y hacer el seguimiento de pacientes con COVID-19. Sin embargo, este análisis depende de la experiencia de los radiólogos, por lo que las valoraciones pueden ser subjetivas. Objetivo. Explorar representaciones de aprendizaje profundo entrenadas con cortes de TC torácica para diferenciar automáticamente entre los casos de COVID-19 y personas no infectadas. Materiales y métodos. Se usaron dos conjuntos de datos de TC: de SARS-CoV-2 CT (conjunto 1) y de la clínica FOSCAL (conjunto 2). Los modelos de aprendizaje supervisados y previamente entrenados en imágenes naturales, se ajustaron usando aprendizaje por transferencia. La clasificación se llevó a cabo mediante aprendizaje de extremo a extremo y clasificadores tales como los árboles de decisiones y las máquinas de soporte vectorial, alimentados por la representación profunda previamente aprendida. Resultados. El enfoque de extremo a extremo alcanzó una exactitud promedio de 92,33 % (89,70 % de precisión) para el conjunto 1 y de 96,99 % (96,62 % de precisión) para el conjunto-2. La máquina de soporte vectorial alcanzó una exactitud promedio de 91,40 % (precisión del 95,77 %) para el conjunto-1 y del 96,00 % (precisión del 94,74 %) para el conjunto 2. Conclusión. Las representaciones profundas lograron resultados sobresalientes al caracterizar patrones radiológicos usados en la detección de casos de COVID-19 a partir de estudios de TC y demostraron ser una potencial herramienta de apoyo del diagnóstico.

Deep learning representations to support COVID-19 diagnosis on CT slices / Representaciones basadas en aprendizaje profundo como apoyo del diagnóstico de la COVID-19 en cortes de tomografía computarizada

Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist's expertise, which may result in subjective evaluations. Objective: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. Materials and methods: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic's dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. Results: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. Conclusion: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.

Introducción. La enfermedad por coronavirus (COVID-19) es actualmente el principal problema de salud pública en el mundo. En este contexto, el análisis automático de tomografías computarizadas (TC) surge como una herramienta diagnóstica complementaria que permite caracterizar hallazgos radiológicos, y categorizar y hacer el seguimiento de pacientes con COVID-19. Sin embargo, este análisis depende de la experiencia de los radiólogos, por lo que las valoraciones pueden ser subjetivas. Objetivo. Explorar representaciones de aprendizaje profundo entrenadas con cortes de TC torácica para diferenciar automáticamente entre los casos de COVID-19 y personas no infectadas. Materiales y métodos. Se usaron dos conjuntos de datos de TC: de SARS-CoV-2 CT (conjunto 1) y de la clínica FOSCAL (conjunto 2). Los modelos de aprendizaje supervisados y previamente entrenados en imágenes naturales, se ajustaron usando aprendizaje por transferencia. La clasificación se llevó a cabo mediante aprendizaje de extremo a extremo y clasificadores tales como los árboles de decisiones y las máquinas de soporte vectorial, alimentados por la representación profunda previamente aprendida. Resultados. El enfoque de extremo a extremo alcanzó una exactitud promedio de 92,33 % (89,70 % de precisión) para el conjunto 1 y de 96,99 % (96,62 % de precisión) para el conjunto-2. La máquina de soporte vectorial alcanzó una exactitud promedio de 91,40 % (precisión del 95,77 %) para el conjunto-1 y del 96,00 % (precisión del 94,74 %) para el conjunto 2. Conclusión. Las representaciones profundas lograron resultados sobresalientes al caracterizar patrones radiológicos usados en la detección de casos de COVID-19 a partir de estudios de TC y demostraron ser una potencial herramienta de apoyo del diagnóstico.

Environmental knowledge, perceived behavioral control, and employee green behavior in female employees of small and medium enterprises in Ensenada, Baja California.

Concern for the environment and the diminishing availability of resources is undoubtedly a relevant issue, both personally and organizationally. That is why knowing the factors that affect the occurrence of green behavior is relevant, particularly in SMEs, due to their importance in the economy of all countries, and specifically by the women who work in them, since their participation is gaining more and more preponderance in the Mexican labor force. It is because of the above that the objective of the research was to analyze the effect of environmental knowledge and perceived behavioral control on the employee green behavior of female employees of small and medium enterprises (SMEs) in Ensenada, Baja California. The study is characterized by having a quantitative approach, a non-experimental, exploratory design and a transverse temporal dimension. 240 questionnaires were applied to female employees of SMEs in Ensenada, Baja California. The data collected was analyzed using structural equation modeling based on the partial least squares technique. The results empirically reflect the following: environmental knowledge has a positive and statistically significant effect on perceived behavioral control and employee green behavior. Also, it was found that the perceived behavioral control variable had a positive and statistically significant effect on the employee green behavior of female employees of SMEs in Ensenada, Baja California. Lastly, it was found that environmental knowledge predicts perceived behavioral control which in turn predicts employee green behavior. In this sense, such findings allow us to consider environmental knowledge and perceived behavioral control as predictive variables of employee green behavior.

Hallazgos histopatológicos pulmonares en COVID-19. Experiencia de autopsias mínimamente invasivas / Pulmonary histopathological findings in Covid-19 patients: based on minimally invasive autopsies

Introducción: la COVID-19 es causada por el virus SARS-CoV-2. La presentación clínica varía desde pacientes asintomáticos hasta manifestaciones severas. Durante la pandemia se han realizado autopsias que han permitido reconocer los cambios en diferentes órganos, siendo el pulmón el más afectado. El objetivo del presente estudio es informar nuestra experiencia en cuanto a los hallazgos histopatológicos pulmonares, mediante el sistema de autopsia mínimamente invasiva. Metodología: se tomaron muestras a 8 pacientes fallecidos por COVID-19 en la unidad de cuidado intensivo (UCI) confirmado por PCR en el Hospital de San José, Bogotá, Colombia, en la primera hora después de la muerte. Los tejidos fueron analizados por dos patólogos en forma independiente. Resultados: se observó en todos daño alveolar difuso (DAD) en fases exudativa, proliferativa o ambas, además de bronconeumonía y neumonitis intersticial. Discusión: el pulmón es el principal órgano afectado por el SARS-CoV-2 y el hallazgo histopatológico más frecuente es el DAD en fases exudativa y mixta. También se han descrito alteraciones en diferentes sistemas. Conclusiones: el hallazgo histopatológico pulmonar más frecuente es el DAD en diferentes estadios. Se considera que la autopsia mínimamente invasiva es de gran utilidad en escenarios donde la convencional se encuentra limitada, pues no presenta grandes restricciones y permite obtener tejidos viables.

Introduction: COVID-19 is caused by the SARS-CoV-2 virus. Its clinical presentation varies from asymptomatic patients to severe manifestations. During the pandemic, autopsies have allowed the recognition of changes in different organs, with the lung being the main affected organ. The aim of this study is reporting our experience derived from pulmonary histopathological findings, based on the minimally invasive autopsy method. Methodology: samples were taken in the immediate postmortem (one hour) period from 8 patients who died with PCR-confirmed COVID-19, in the intensive care unit (ICU) of Hospital de San José, Bogotá, Colombia. Tissue specimens were analyzed independently by two pathologists. Results: all specimens exhibited, diffuse alveolar damage (DAD) in exudative or proliferative phases, or both, in addition to bronchopneumonia and interstitial pneumonitis. Discussion: the lung is the main organ affected by SARS-CoV-2 and the most frequent histopathologic finding is exudative phase or various phases DAD. Alterations in different systems have also been described. Conclusions: the most frequent histopathological finding is DAD in any of its phases. Minimally invasive autopsy is considered a very useful alternative when conventional autopsy cannot be performed, since it does not have major restrictions and allows collecting viable samples.

The Implications of Membranes Used as Separators in Microbial Fuel Cells.

Microbial fuel cells (MFCs) are electrochemical devices focused on bioenergy generation and organic matter removal carried out by microorganisms under anoxic environments. In these types of systems, the anodic oxidation reaction is catalyzed by anaerobic microorganisms, while the cathodic reduction reaction can be carried out biotically or abiotically. Membranes as separators in MFCs are the primary requirements for optimal electrochemical and microbiological performance. MFC configuration and operation are similar to those of proton-exchange membrane fuel cells (PEMFCs)-both having at least one anode and one cathode split by a membrane or separator. The Nafion® 117 (NF-117) membrane, made from perfluorosulfonic acid, is a membrane used as a separator in PEMFCs. By analogy of the operation between electrochemical systems and MFCs, NF-117 membranes have been widely used as separators in MFCs. The main disadvantage of this type of membrane is its high cost; membranes in MFCs can represent up to 60% of the MFC's total cost. This is one of the challenges in scaling up MFCs: finding alternative membranes or separators with low cost and good electrochemical characteristics. The aim of this work is to critically review state-of-the-art membranes and separators used in MFCs. The scope of this review includes: (i) membrane functions in MFCs, (ii) most-used membranes, (iii) membrane cost and efficiency, and (iv) membrane-less MFCs. Currently, there are at least 20 different membranes or separators proposed and evaluated for MFCs, from basic salt bridges to advanced synthetic polymer-based membranes, including ceramic and unconventional separator materials. Studies focusing on either low cost or the use of natural polymers for proton-exchange membranes (PEM) are still scarce. Alternatively, in some works, MFCs have been operated without membranes; however, significant decrements in Coulombic efficiency were found. As the type of membrane affects the performance and total cost of MFCs, it is recommended that research efforts are increased in order to develop new, more economic membranes that exhibit favorable properties and allow for satisfactory cell performance at the same time. The current state of the art of membranes for MFCs addressed in this review will undoubtedly serve as a key insight for future research related to this topic.

Estrategias de flexibilidad y su relación con el ambiente laboral, la dedicación y el reto intelectual / Flexibility strategies and their relationship with workplace environment, dedication and intellectual challenge

Resumen Las empresas han integrado estrategias de flexibilidad como parte importante en los procesos de recursos humanos, principalmente como un componente más de su salario emocional. El objetivo de esta investigación fue establecer de qué manera la flexibilidad laboral se relaciona con la dedicación, el reto intelectual y el ambiente laboral que perciben las personas en su lugar de trabajo. Se realizó una investigación de tipo cuantitativo durante el mes de noviembre de 2018, en el que se aplicó un cuestionario en línea de 38 reactivos, obteniendo una muestra de 282 trabajadores mexicanos de la ciudad de Monterrey, Nuevo León, México. El 95.4 % de los entrevistados manifestaron interés por al menos una de las variantes de trabajo flexible. La modalidad de trabajo en espacios abiertos fue la opción menos aceptada, con una preferencia del 7.8 %. Se encontró correlación positiva entre el ambiente laboral y el balance positivo por trabajo flexible. El ambiente laboral también mostró alta correlación positiva con la dedicación y el reto intelectual. Lo anterior resulta de interés en el contexto actual para las estrategias de recursos humanos y el ambiente empresarial.

Abstract Currently, there are many companies in the world that have been integrating flexibility strategies as an important part in the human resources processes, mainly as an additional component of their emotional salary. The aim of this research is to understand the correlation between job flexibility and dedication, intellectual challenge and the work environment that people perceive in their workplace. A quantitative research was carried out during November 2018. An online questionnaire of 38 items was administered to a sample of 282 Mexican workers from the city of Monterrey, Nuevo León in Mexico. Findings indicate that 95.4 % of interviewees showed an interest for at least one of the flexible work variants. The open space work modality was the least desirable with a 7.8 % preference. A positive correlation between work environment and a positive balance due to flexible work was found. The working environment similarly showed a high positive correlation with dedication and intellectual challenge. The foregoing findings are of interest to the current context for human resource strategies and the business environment.

Patología y salud pública: Oportunidades para el avance / Pathology and public health: opportunities for progress

La patología y la salud pública son disciplinas que se complementan en múltiples formas, desde la información que aportan mutuamente a niveles individual y poblacional, hasta la elaboración de políticas públicas en salud y la gestión de la información en los biobancos, así como la articulación para respuesta en emergencias y brotes. En revisión no sistemática resaltamos que los dos campos de mayor colaboración con la salud pública son la patología forense (comprendiendo muertes violentas y no violentas) y la patología molecular, realizando aportes significativos a la planeación de los servicios de salud, la calidad de la información epidemiológica, la salud pública basada en la evidencia que permite una mejor toma de decisiones, y la gestión de la salud comunitaria y poblacional. A partir de la revisión realizada se identificaron como puntos de mejora el uso de los sistemas de información, la necesidad de un enfoque interdisciplinario más tangible, y la urgente transformación educativa que subyace a esta colaboración.

Pathology and public health are disciplines that complement each other in many ways, from the information they provide to each other at the individual and population levels, to the development of public health policies and the management of information in biobanks, as well as articulation in responding to emergencies and outbreaks. Our non-systematic review highlights that the two most relevant fields which collaborate with public health are forensic pathology (including violent and non-violent deaths) and molecular pathology, making significant contributions to health care planning, the quality of epidemiological information, evidence-based public health that enables better decision-making, and community and population health management. This review identified the use of information systems, the need for a more tangible interdisciplinary approach, and the urgent educational transformation that underlies this collaboration, as areas for improvement.

Clinical features and outcomes of thoracic surgery patients during the COVID-19 pandemic.

OBJECTIVES: The goal of this study was to describe the clinical features and outcomes of thoracic surgery patients during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: Thirty-five patients were treated at the 12 de Octubre University Hospital in Madrid between 1 March 2020 and 24 April 2020 during the COVID-19 pandemic. Patient demographics, surgical procedures, complications, COVID-19 symptoms and outcomes were recorded. A protocol was introduced to reduce the risk of operating on patients with COVID-19, including symptom screening, a polymerase chain reaction test for severe acute respiratory syndrome coronavirus 2 and computed tomography scans of the chest. Surgical activity changed significantly during this time, from an initial period of near-normal activity, through an emergency-only period and finally a recovery period when some oncological surgical cases were restarted. Selection criteria for surgical patients are also described. RESULTS: A total of 34 patients underwent surgery during the pandemic period. We performed 22 lung resections (11 lobectomies and 11 sublobar resections). No hospital deaths were recorded. An elective surgery patient and an emergency surgery patient were diagnosed with COVID-19 (5.88%). The former died within 30 days after surgery. CONCLUSIONS: Severe acute respiratory syndrome coronavirus 2 represents a tremendous limitation for thoracic surgical practice. Preoperative practices to exclude asymptomatic cases infected with the virus allowed us to perform thoracic surgical procedures.

Bioimmobilization of toxic metals by precipitation of carbonates using Sporosarcina luteola: An in vitro study and application to sulfide-bearing tailings.

Metal release from mining wastes is a major environmental problem affecting ecosystems that requires effective, low-cost strategies for prevention and reclamation. The capacity of two strains (UB3 and UB5) of Sporosarcina luteola was investigated to induce the sequestration of metals by precipitation of carbonates in vitro and under microcosm conditions. These strains carry the ureC gene and have high urease activity. Also, they are highly resistant to metals and have the capacity for producing metallophores and arsenophores. SEM, EDX and XRD reveal that the two strains induced precipitation of calcite, vaterite and magnesian calcite as well as several (M2+)CO3 such as hydromagnesite (Mg2+), rhodochrosite (Mn2+), cerussite (Pb2+), otavite (Cd2+), strontianite (Sr2+), witherite (Ba2+) and hydrozincite (Zn2+) in vitro. Inoculation of the mixed culture of UB3+UB5 in tailings increased the pH and induced the precipitation of vaterite, calcite and smithsonite enhancing biocementation and reducing pore size and permeability slowing down the oxidation of residual sulfides. Results further demonstrated that the strains of S. luteola immobilize bioavailable toxic elements through the precipitation and coprecipitation of thermodynamically stable (M2+)CO3, Fe-Mn oxyhydroxides and organic chelates.

Effects of sodium butyrate and active Bacillus amyloliquefaciens supplemented to pasteurized waste milk on growth performance and health condition of Holstein dairy calves.

The objectives of the present study were to examine the impact of feeding both probiotics and sodium butyrate on calf performance and the economic implication of each treatment. A completely randomized design was used to investigate body weight (BW) gain, feed conversion efficiency and health conditions of Holstein dairy calves fed either pasteurized waste milk (PWM; n = 9) or a non-medicated milk replacer containing sodium butyrate and active probiotic Bacillus amyloliquefaciens (NMR; n = 9) from birth to 60 days of age. Numerically, calves fed PWM consumed more starter feed between days 16 and 45 than calves fed NMR but the difference became smaller by 60 days. Birth weights and colostrum IgG and serum total protein concentrations did not differ (p > 0.05) between the PWM and NMR calves. Calves receiving PWM had slightly greater BW at days 30 and 45, but were similar to that of calves receiving NMR at day 60. No differences were observed between PWM and NMR-calves for BW gains, flank height, hip width and health conditions (p < 0.05). Calves fed NMR had more watery feces but less frequent bouts of coughing than PWM-fed calves. Feed cost was higher (p < 0.001) for PWM-fed calves than NMR-fed calves during the experimental period. Dairy calves receiving NMR fortified with sodium butyrate and Bacillus probiotic could perform as similar as calves receiving PWM, and they had similar economic efficiency during the 60-d study period.