Simulated Inhibitory Effects of Typical Byproducts of Biomass Pretreatment Process on the Viability of Saccharomyces cerevisiae and Bioethanol Production Yield

The abundance of second generation feedstock reinforces the consideration of biofuel over fossil fuel, as bioethanol can be produced from lignocellulosic materials. However, the pretreatment required for oxidation of lignocellulose into hexose often results in the production of inhibitors likely to impede the activity of Saccharomyces cerevisiae during bioethanol production. This study aimed to investigate the comparative inhibitory effects of acetic acid and vanillin on the viability of S. cerevisiae and the production yield of bioethanol. Different concentrations of inhibitors were spiked in the fermentation broth then the production of bioethanol monitored overtime and correlated with cell viability. The results showed that the inhibition of S. cerevisiae by vanillin is more potent compared to acetic acid; however the reduction of bioethanol yield after 12 h was more pronounced with acetic acid (42.8% reduction) than with vanillin (33.3% reduction) which was ascribed to the simultaneous production of weak acids during the fermentation process. The viability test has shown that in the presence of lower concentrations of inhibitors, S. cerevisiae can adapt for the first 12 h of fermentation and then may improve ethanol production yield overtime. At lower concentrations (2 g/l vanillin and 4 g/l acetic acid) the effect of inhibitors on the viability of S. cerevisiae and ethanol productivity does not last and can be overcome by the adaptation of the yeast. However, the presence of higher concentrations (4 g/l vanillin and 6 g/l acetic acid) results to nearly total inhibition of bioethanol production and the remediation of such effect may therefore require a detoxification process.Keywords: Bioethanol Production, Saccharomyces cerevisiae, Inhibition, Acetic Acid, Vanillin, Cell Viabilit

Integrating RFID Technology and EPC Network into a B2B Retail Supply Chain: A Step Toward Intelligent Business Processes

This article introduces RFID technology and the EPC Network and investigates their potential for B-to-B eCommerce supply chain management. Based on empirical data gathered from four tightly interrelated firms from three layers of a supply chain, several scenarios integrating RFID and the EPC Network have been tested and evaluated. In the context of warehousing activities in one specific retail supply chain, the results indicate that i) the business process approach seems quite appropriate to capture the real potential of RFID and the EPC Network; ii) RFID technology and the EPC Network can improve the “shipping” and the “receiving” processes; iii) they can automatically trigger some business processes; iv) they foster a higher level of information sharing between supply chain members; and v) they promote the emergence of new business processes such as “process-to-process,” “process-to-machine,” and “machine-to-machine.” The paper helps to improve our understanding of the real potential of RFID and the EPC Network for business processes.Este artículo introduce a los conceptos de la tecnología RFID y las redes EPC, e investiga su potencial para la gestión de cadenas de proveedores en el comercio electrónico B-a-B. basado en datos empíricos recolectados desde compañías estrechamente relacionadas que provienen de tres distintas capas de una cadena de proveedores. En el contexto de las actividades de actividades de almacenamiento en una cadena de proveedores específica de retail, los resultados indican que (i) la aproximación al proceso de negocio se ve muy apropiado para capturar el real potencial de la RFID y la red EPC; (ii) La tecnología RFID y las redes EPC pueden mejorar los procesos de “envío” y “recepción”; (iii) pueden automáticamente gatillar algunos procesos de negocios; (iV) impulsan un nivel más alto de repartición entre los miembros de la cadena de abastecimiento; y (v) promueven el surgimiento de nuevos procesos de negocios tales como “proceso-a-proceso”, “ proceso-a-máquina”, y “máquina-a-máquina”. Este artículo ayuda a mejorar nuestro entendimiento del potencial real de la tecnología RFID y las redes EPC para los procesos de negocio.This article introduces RFID technology and the EPC Network and investigates their potential for B-to-B eCommerce supply chain management. Based on empirical data gathered from four tightly interrelated firms from three layers of a supply chain, several scenarios integrating RFID and the EPC Network have been tested and evaluated. In the context of warehousing activities in one specific retail supply chain, the results indicate that i) the business process approach seems quite appropriate to capture the real potential of RFID and the EPC Network; ii) RFID technology and the EPC Network can improve the “shipping” and the “receiving” processes; iii) they can automatically trigger some business processes; iv) they foster a higher level of information sharing between supply chain members; and v) they promote the emergence of new business processes such as “process-to-process,” “process-to-machine,” and “machine-to-machine.” The paper helps to improve our understanding of the real potential of RFID and the EPC Network for business processes

Management at the service of research: ReOmicS, a quality management system for omics sciences

Management and research represent a binomial almost unknown, whose potentialities and requirements have not yet been fully exploited even if, recently, the scientific and social communities have felt the burden of producing results and data requiring at the same time reproducibility, reliability, safety and efficacy of the discoveries, as well as a profitable use of resources. A Quality Management System (QMS) could represent a valid tool for these purposes, improving the quality of the research. The research community could ask whether and how it is possible to apply this approach in a research laboratory without hindering their creativity, and what the possible benefits might be. On the other hand, an international standard for a quality management system appropriate for a research laboratory is yet to come. The choice, the design and the application of a QMS, inspired by the Good Laboratory Practices, in a research laboratory specialized on “omics” sciences, is fully described in this paper. Its application has already shown good outcomes as testified by specific metric of efficiency and effectiveness. The approach is innovative as there is no obvious requirement for research laboratories to develop and define quality objectives. The paper highlights how the QMS approach enhances the relationship with public and private sectors by increasing customer confidence and loyalty, as well as improving the overall performance of the laboratory in terms of throughput and value of research. These results encourage proposing it as a QMS model providing a new and scalable operational strategy to be applied in a research environment with the same target and even in a generic research laboratory

Integrating RFID technology and EPC network into a B2B retail supply chain: a step toward intelligent business processes

This article introduces RFID technology and the EPC Network and investigates their potential for B-to-B eCommerce supply chain management. Based on empirical data gathered from four tightly interrelated firms from three layers of a supply chain, several scenarios integrating RFID and the EPC Network have been tested and evaluated. In the context of warehousing activities in one specific retail supply chain, the results indicate that i) the business process approach seems quite appropriate to capture the real potential of RFID and the EPC Network; ii) RFID technology and the EPC Network can improve the “shipping” and the “receiving” processes; iii) they can automatically trigger some business processes; iv) they foster a higher level of information sharing between supply chain members; and v) they promote the emergence of new business processes such as “process-to-process,” “process-to-machine,” and “machine-to-machine.” The paper helps to improve our understanding of the real potential of RFID and the EPC Network for business processes

Accurate detection and quantification of SARS-CoV-2 genomic and subgenomic mRNAs by ddPCR and meta-transcriptomics analysis

SARS-CoV-2 replication requires the synthesis of a set of structural proteins expressed through discontinuous transcription of ten subgenomic mRNAs (sgmRNAs). Here, we have fine-tuned droplet digital PCR (ddPCR) assays to accurately detect and quantify SARS-CoV-2 genomic ORF1ab and sgmRNAs for the nucleocapsid (N) and spike (S) proteins. We analyzed 166 RNA samples from anonymized SARS-CoV-2 positive subjects and we observed a recurrent and characteristic pattern of sgmRNAs expression in relation to the total viral RNA content. Additionally, expression profiles of sgmRNAs, as determined by meta-transcriptomics sequencing of a subset of 110 RNA samples, were highly correlated with those obtained by ddPCR. By providing a comprehensive and dynamic snapshot of the levels of SARS-CoV-2 sgmRNAs in infected individuals, our results may contribute a better understanding of the dynamics of transcription and expression of the genome of SARS-CoV-2 and facilitate the development of more accurate molecular diagnostic tools for the stratification of COVID-19 patients

A primer on machine learning techniques for genomic applications

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous “omic” data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available

The next step in the bioremediation of heavy metal polluted water: development of suitable microbial-sorbent

Abstract In this study needful changes are inflicted on potential microbial sorbent in order to facilitate it use industrially for remediation of heavy metal pollution of water sources. Bacillaceae bacterium isolated from mining areas was transformed with cnr gene coding for nickel and cobalt resistance. Nickel recovery ability of the wild strain was then improved and prediction of adsorption behaviour made possible

MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics