Economic analysis of the supercritical fluid extraction of lupane-triterpenoids from Acacia dealbata Link bark

The supercritical fluid extraction of Acacia dealbata Link. bark was performed with carbon dioxide at different pressures (10–30 MPa), temperatures (40–80 °C), and cosolvents contents (0–10 wt% of ethyl acetate or ethanol) to analyse their effect on various performance indicators whose best values were: total extraction yield of 1.57 wt%; lupenyl acetate (LA) and lupenone (Lu) extraction yields of 777.5 mg kgbark-1 and 679.8 mg kgbark-1; and LA and Lu extract concentrations of 15.8 wt% and 12.8 wt%. Two extraction curves were measured, and an economic analysis was accomplished. The impact of pressure, temperature, cosolvent content, and extraction time on the cost of manufacturing (COM) and productivity were evaluated, reaching a minimum of 52.3 € kgextract-1. A sensitivity analysis showed that the electricity price is the most impactful variable on the COM. Overall, the increase of pressure, temperature and cosolvents content favoured the productivity, decreasing the production cost.publishe

Rastall Cosmology and the \Lambda CDM Model

Rastall's theory is based on the non-conservation of the energy-momentum tensor. We show that, in this theory, if we introduce a two-fluid model, one component representing vacuum energy whereas the other pressureless matter (e.g. baryons plus cold dark matter), the cosmological scenario is the same as for the \Lambda CDM model, both at background and linear perturbative levels, except for one aspect: now dark energy may cluster. We speculate that this can lead to a possibility of distinguishing the models at the non-linear perturbative level.Comment: 9 pages, 1 figure. Accepted for publication in Physical Review

Simulation and techno-economic optimization of the supercritical CO2 extraction of Eucalyptus globulus bark at industrial scale

This work addresses economic and process aspects of the supercritical fluid extraction (SFE) of Eucalyptus globulus bark at industrial scale. Broken plus intact cells (BIC) model was applied to existing data and new SFE curves were simulated. Then, statistical optimization was performed with Response Surface Methodology (RSM) involving of 5 factors (pressure, temperature, cosolvent content, solvent flow rate and extraction time), and four responses: Total Yield (ηTotal), Productivity, Cost of Manufacturing (COM) and Process Energy. The design and simulation of the industrial process (Aspen Plus® software) was performed including the employment of cosolvent in the system. The best COM scored 28.1 € kgextract-1, where ηTotal was 0.84-0.96 wt.%, Productivity reached 311–362 tonextractyear-1, and Process Energy scored 1.46– 2.10 GJ kgextract-1. These results underline that SFE provides an extended margin for trade-offs, and arguments towards the integration of SFE technology to biorefine the bark of E. globulus in pulp mills.publishe

Sarcosine oxidase composite screen-printed electrode for sarcosine determination in biological samples

XIX Meeting of the Portuguese Electrochemical Society - XVI Iberic Meeting of ElectrochemistryProstate Cancer (PCa) is the most common form of cancer in men in Europe with a 61.4 % incidence among all cancer cases and a 12.1 % mortality [1] and, therefore, its early detection is fundamental for increasing the survival rate. Currently, diagnosis and management of patients with PCa is only based on the determination of the biomarker Prostate Specific Antigen (PSA). However, the method used for PCa detection has poor sensitivity and specificity, leading to false negative and false positive test results and many patients are sent to unnecessary biopsy procedures [2]. Therefore, there is a need to seek for new biomarkers and more effective screening. In this work, a biosensor device was developed for the quantification of sarcosine via electrochemical detection of H2O2 (at 0.6 V) generated from the catalyzed oxidation of sarcosine. The detection was carried out after the modification of carbon screen printed electrodes (SPEs) by immobilization of sarcosine oxidase (SOX) on the electrode surface. The strategies used herein included the activation of the carbon films by an electrochemical step and the formation of an NHS/EDAC layer to bond the enzyme to the electrode, the use of metallic or semiconductor nanoparticles layer previously or during the enzyme immobilization. In order to improve the sensor stability and selectivity a polymeric layer with extra enzyme content was further added. The proposed methodology for the detection of sarcosine allowed obtaining a limit of detection (LOD) of 1.6x10-5 mM, using a linear concentration range between 1x10-5 and 1x10-4 mM. The biosensor was successfully applied to the analysis of sarcosine in urine samples.

Proposal of a virtual collaborative news environment: an interdisciplinary study

The main objective of this work is to demonstrate the advances made by the CATI research group with regard to the proposal to create a virtual collaborative news environment (AVNC) , to be initially implemented at the Northern Fluminense Darcy Ribeiro State University(UENF) through the Postgraduate Program in Cognition and Language. This interdisciplinary environment involves concepts from the area of communication, administration and information technology and seeks to meet the demands of generation, storage, retrieval, processing and transmission of information in this digital age. The AVNC involves three research fronts: rethinking the logic of news production, structuring a platform that is collaborative and defining what a business and management model should be that can give sustainability to this environment. In today\u27s world, it is important to consider the reader\u27s increasingly active participation in journalism, through information and communication technologies, thanks to the social changes that come with them. In times of cyberspace and cyberculture, it is necessary to rethink the dynamics of journalistic production. For this, authors like Pierre Lévy, Lúcia Santaella, Henry Jenkins, Caio Túlio Costa, among others were searched. In an era in which forms of collaborative ownership prevail, the platform being considered in this research follows the 3C collaboration model, according to Michalsky, Mamani, and Gerosa. A global platform where individuals interact, communicate, collaborate and gather information requires a business model and management that assists in managing the collaborative virtual news environment. For this, we used authors such as Alex Osterwalder, Eric Flamholtz, Siqueira and Crispim, Campos, among others. With this, an increasingly intelligent and collaborative environment is expected, with the active participation of all the agents involved. That is, what is proposed is the total interaction of the Internet user-reader

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase.

Funder: State Government of VictoriaKinases play crucial roles in cellular signalling and biological processes with their dysregulation associated with diseases, including cancers. Kinase inhibitors, most notably those targeting ABeLson 1 (ABL1) kinase in chronic myeloid leukemia, have had a significant impact on cancer survival, yet emergence of resistance mutations can reduce their effectiveness, leading to therapeutic failure. Limited effort, however, has been devoted to developing tools to accurately identify ABL1 resistance mutations, as well as providing insights into their molecular mechanisms. Here we investigated the structural basis of ABL1 mutations modulating binding affinity of eight FDA-approved drugs. We found mutations impair affinity of type I and type II inhibitors differently and used this insight to developed a novel web-based diagnostic tool, SUSPECT-ABL, to pre-emptively predict resistance profiles and binding free-energy changes (ΔΔG) of all possible ABL1 mutations against inhibitors with different binding modes. Resistance mutations in ABL1 were successfully identified, achieving a Matthew's Correlation Coefficient of up to 0.73 and the resulting change in ligand binding affinity with a Pearson's correlation of up to 0.77, with performances consistent across non-redundant blind tests. Through an in silico saturation mutagenesis, our tool has identified possibly emerging resistance mutations, which offers opportunities for in vivo experimental validation. We believe SUSPECT-ABL will be an important tool not just for improving precision medicine efforts, but for facilitating the development of next-generation inhibitors that are less prone to resistance. We have made our tool freely available at http://biosig.unimelb.edu.au/suspect_abl/

Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)

Spatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response, and robust methods for modeling new wildfire occurrences are ever-emerging. Here, ignition locations of natural and human-caused wildfires across the state of Montana (USA) from 1992 to 2017 were intersected with static, 30 m resolution spatial data that captured topography, fuel availability, and human transport infrastructure. Once combined, the data were used to train several simple and multiple logistic generalized linear models (GLMs) and generalized additive models (GAMs) to predict the spatial likelihood of natural and human-caused ignitions. Increasingly more complex models that included spatial smoothing terms were better at distinguishing locations with and without natural and human-caused ignitions, achieving area under the receiver operating characteristic curves (AUCs) of 0.84 and 0.89, respectively. Whilst both ignition types were more likely to occur at intermediate fuel loads, as characterized by the local maximum Normalized Difference Vegetation Index (NDVI), naturally-ignited wildfires were more locally influenced by slope, while human-caused wildfires were more locally influenced by distance to roads. Static maps of ignition likelihood were verified by demonstrating that mean annual ignition densities (# yr−1 km−1) were higher within areas of higher predicted probabilities. Although the spatial models developed herein only address the static component of wildfire hazard, they provide a foundation upon which dynamic data can be superimposed to forecast and map wildfire ignition probabilities statewide on a timely basis.This research funded in part by Project FIREPATHS (PID2020-116556RA-I00), Spanish Ministry of Science and Innovation

Structure guided prediction of Pyrazinamide resistance mutations in pncA

Abstract: Pyrazinamide plays an important role in tuberculosis treatment; however, its use is complicated by side-effects and challenges with reliable drug susceptibility testing. Resistance to pyrazinamide is largely driven by mutations in pyrazinamidase (pncA), responsible for drug activation, but genetic heterogeneity has hindered development of a molecular diagnostic test. We proposed to use information on how variants were likely to affect the 3D structure of pncA to identify variants likely to lead to pyrazinamide resistance. We curated 610 pncA mutations with high confidence experimental and clinical information on pyrazinamide susceptibility. The molecular consequences of each mutation on protein stability, conformation, and interactions were computationally assessed using our comprehensive suite of graph-based signature methods, mCSM. The molecular consequences of the variants were used to train a classifier with an accuracy of 80%. Our model was tested against internationally curated clinical datasets, achieving up to 85% accuracy. Screening of 600 Victorian clinical isolates identified a set of previously unreported variants, which our model had a 71% agreement with drug susceptibility testing. Here, we have shown the 3D structure of pncA can be used to accurately identify pyrazinamide resistance mutations. SUSPECT-PZA is freely available at: http://biosig.unimelb.edu.au/suspect_pza/

Characterization of levan produced by a Paenibacillus sp. isolated from Brazilian crude oil

A levan-type fructooligosaccharide was produced by a Paenibacillus strain isolated from Brazilian crude oil, the purity of which was 98.5% after precipitation with ethanol and dialysis. Characterization by FTIR, NMR spectroscopy, GC-FID and ESI-MS revealed that it is a mixture of linear (26) fructosyl polymers with average degree of polymerization (DP) of 18 and branching ratio of 20. Morphological structure and physicochemical properties were investigated to assess levan microstructure, degradation temperature and thermomechanical features. Thermal Gravimetric Analysis highlighted degradation temperature of 218°C, Differential Scanning Calorimetry (DSC) glass transition at 81.47°C, and Dynamic Mechanical Analysis three frequency-dependent transition peaks. These peaks, corresponding to a first thermomechanical transition event at 86.60°C related to the DSC endothermic event, a second at 170.9°C and a third at 185.2°C, were attributed to different glass transition temperatures of oligo and polyfructans with different DP. Levan showed high morphological versatility and technological potential for the food, nutraceutical, and pharmaceutical industries.São Paulo Research Foundation (FAPESP) within Grants 2020/13271-6 and 2018/25511-1. Additionally, the authors acknowledge the financial support by National Council for Scientific and Technological Development – CNPq and by Coordination for the Improvement of Higher Education Personnel (CAPES), Finance Code 001. In addition, this study was supported by the Portuguese Science Foundation (FCT) under the scope of the strategic funding of UID/BIO/04469/2020 unit and the Project FOSYNBIO (POCI-01-0145-FEDER-029549).info:eu-repo/semantics/publishedVersio