Sustainable bioethanol production using agro-industrial by-products

This work aimed to evaluate a sustainable bioethanol production by a laboratorial isolate strain of Saccharomyces cerevisiae, along with the use of agro-industrial by-products as carbon source. The effect of several carbon sources and their concentrations was studied using carob pod extract (CPE) and beet molasses (BM) and compared with glucose and sucrose as conventional carbohydrates at different concentrations, 15, 20 and 30 g/l.No significant difference was found between maximum ethanol production obtained with CPE, BM, glucose and sucrose fermentations profiles. It was obtained values of 10.65 g/l and 10.5 g/l ethanol, respectively for sucrose and CPE at 30g/l, which can be improved using higher substrate concentration

Preliminary study on the effect of fermented cheese whey on Listeria monocytogenes, Escherichia coli O157:H7 and Salmonella Goldcoast populations inoculated onto fresh organic lettuce

Cheese whey fermented by an industrial starter consortium of lactic acid bacteria was evaluated for its antibacterial capacity to control a selection of pathogenic bacteria. For their relevance on outbreak reports related to vegetable consumption, this selection included Listeria monocytogenes, serotype 4b, Escherichia coli O157:H7, and Salmonella Goldcoast. Organically grown lettuce was inoculated with an inoculum level of *107 colonyforming unit (CFU)/mL and was left for about 1 h in a safety cabinet before washing with a perceptual solution of 75:25 (v/v) fermented whey in water, for 1 and 10 min. Cells of pathogens recovered were then counted and their number compared with that obtained for a similar treatment, but using a chlorine solution at 110 ppm. Results show that both treatments, either with chlorine or fermented whey, were able to significantly reduce ( p < 0.05) the number of bacteria, in a range of 1.15–2.00 and 1.59–2.34 CFU/g, respectively, regarding the bacteria tested. Results suggest that the use of fermented whey may be as effective as the solution of chlorine used in industrial processes in reducing the pathogens under study (best efficacy shown for Salmonella), with the advantage of avoiding health risks arising from the formation of carcinogenic toxic chlorine derinfo:eu-repo/semantics/publishedVersio

Mycotoxin contamination of Maize and Guinea corn from markets in Plateau State, Nigeria

Maize (Zea mays) and guinea corn (Sorghum bicolor) are major food items in Plateau state, Nigeria. A multistage sampling technique was used to select the markets and store/warehouses used for this study; sample collection employed a simple random sampling method from different sampling points within designated areas. A total of 18 representative samples were collected and analyzed for the following mycotoxins: aflatoxins (Aflatoxin B1 - AFB1, Aflatoxin B2 - AFB2, Aflatoxin G1 - AFG1 and Aflatoxin G2 - AFG2), fumonisins (Fumonisin B1 - FB1 and Fumonisin B2 - FB2 ) and cyclopiazonic acid (CPA). Out of 12 samples analyzed for Aflatoxins, AFB1 was detected in 5, AFB2 in 1, AFG1 in 1 and AFG2 in 6 samples respectively. The highest concentration of AFB1 and AFG2 were found in maize samples from Pankshin market. Only maize samples from Mangu market were contaminated with AFB2 and also harboured the lowest concentration of AFG2. AFG1 contamination occurred in only guinea corn from Shendam market. and FB1 was detected in all 18 samples analyzed. The mycotoxin CPA was not detected in any of the samples. Aflatoxins levels in analyzed samples were regarded as safe based on Nigerian and European Union maximum permissible levels of 4g/kg. With the exception of two samples, FB1 levels in analyzed maize samples were within European Union maximum permissible levels of 1,000 to 3000g/kg. The health and food safety implications of these results for the human and animal population are further discussed

Identification of mycotoxigenic fungi from grains in a Nigerian region using the modern polyphasic methodology

Mycotoxins are poisonous substances produced by fungi which contaminate agricultural commodities. Many foods and feeds can become contaminated with mycotoxins since they can form in commodities before harvest, during the time between harvesting and drying, and in storage. The food crops most often affected include maize, peanuts, sorghum, wheat, cocoa and tree nuts. Mycotoxins may also be carried over to animal products due to consumption of contaminated feed. Maize (Zea mays) and guinea corn (Sorghum bicolor) form a major staple of the study area and are high risk commodities for mycotoxigenic fungi and mycotoxin contamination. Multistage sampling technique was used to select the markets and store/warehouses used for this study; sample collection employed a simple random sampling method from different sampling points within designated areas. Identification of all fungal isolates was carried out using the modern polyphasic methodology for filamentous fungi identification. At the level of phenotypic approach, the mycotoxigenic fungi Aspergillus, Fusarium, and Penicillium were identified. These fungal isolates also produced the mycotoxins Aflatoxins B1 and B2, Fumonisin B1, Cyclopiazonic acid, Ochratoxin A and Ochratoxin alfa. Spectral analysis by MALDI-TOF MS identified the Aspergillus species as A. flavus, A. aculeatus, A. niger and A. tamarii. Work is currently on-going to complete fungal identification for all isolates down to species level using the genotypic approach. In view of the toxic effects of mycotoxin contamination, the isolation, identification and characterization of mycotoxigenic fungi from maize and guinea corn in the study area pose serious health risks for the human and animal population and also have implications for food safety and public health in Nigeria

Integrated feature selection and classification algorithm in the prediction of work-related accidents in the retail sector: a comparative study

Assessing the different factors that contribute to accidents in the workplace is essential to ensure the safety and well-being of employees. Given the importance of risk identification in hazard prediction, this work proposes a comparative study between different feature selection techniques (χ2 test and Forward Feature Selection) combined with learning algorithms (Support Vector Machine, Random Forest, and Naive Bayes), both applied to a database of a leading company in the retail sector, in Portugal. The goal is to conclude which factors of each database have the most significant impact on the occurrence of accidents. Initial databases include accident records, ergonomic workplace analysis, hazard intervention and risk assessment, climate databases, and holiday records. Each method was evaluated based on its accuracy in the forecast of the occurrence of the accident. The results showed that the Forward Feature Selection-Random Forest pair performed better among the assessed combinations, considering the case study database. In addition, data from accident records and ergonomic workplace analysis have the largest number of features with the most significant predictive impact on accident prediction. Future studies will be carried out to evaluate factors from other databases that may have meaningful information for predicting accidents.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). This work has been supported by NORTE-01-0247-FEDER-072598 iSafety: Intelligent system for occupational safety and well-being in the retail sector. Inˆes Sena was supported by FCT PhD grant UI/BD/153348/2022.info:eu-repo/semantics/publishedVersio

Nature through the eyes of chemistry: characterization of novel active molecules from Azorean Natural Products

Jornadas "Ciência nos Açores – que futuro?", Ponta Delgada, 7-8 de Junho de 2013.A área de Química de Produtos Naturais tem contribuído, grandemente, para a descoberta de fármacos e agroquímicos, entre outros, por meio do isolamento de substâncias ativas produzidas por diversos organismos. As novas moléculas têm grande interesse científico, económico, social e farmacológico podendo contribuir, em muito, para a saúde das pessoas e para a sua qualidade de vida. O rastreio de novos compostos com atividade biológica tem sido, recentemente, uma das prioridades da comunidade científica e os Açores, pelas suas características particulares e endemismos assumem particular importância nesta área de investigação. O estudo das potencialidades de plantas e organismos marinhos dos Açores tem sido desenvolvido na Universidade dos Açores/CIRN desde a década de noventa, usando a estratégia de investigação seguinte: (i) seleção dos organismos mais promissores para fins industriais e comerciais pela avaliação da sua atividade em ensaios biológicos diversos, (ii) extração, separação e identificação dos compostos bioativos e (iii) estudos da relação estrutura-atividade dos compostos bioativos isolados.ABSTRACT: The chemistry of Natural Products has contributed greatly to the discovery of pharmaceuticals and agrochemicals agents, among others, by the isolation of active substances produced by various organisms. The new molecules are of great scientific, economic, social and pharmacological importance and may contribute greatly to the people's health and their quality of life. The screening for new compounds with biological activity has been a recent priority for the scientific community and the Azores, by its particular characteristics and endemism is of crucial importance in this area of research. The study of the Azorean plants and marine organisms potential has been investigated in Azores University/CIRN since the '90s decade, using the following research strategy: (i) screening of the most promising organisms for industrial and commercial purposes by evaluating their activity in several biological assays, (ii) extraction, purification and molecular structure elucidation of the bioactive compounds, and (iii) studies of structure-activity relationships of the isolated bioactive compounds

An integer programming approach for sensor location in a forest fire monitoring system

Forests worldwide have been devastated by fires. Forest fires cause incalculable damage to fauna and flora. In addition, a forest fire can lead to the death of people and financial damage in general, among other problems. To avoid wildfire catastrophes is fundamental to detect fire ignitions in the early stages, which can be achieved by monitoring ignitions through sensors. This work presents an integer programming approach to decide where to locate such sensors to maximize the coverage provided by them, taking into account different types of sensors, fire hazards, and technological and budget constraints. We tested the proposed approach in a real-world forest with around 7500 locations to be covered and about 1500 potential locations for sensors, showing that it allows obtaining optimal solutions in less than 20 min.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and within project PCIF/GRF/0141/2019 “O3F - An Optimization Framework to reduce Forest Fire” and also the project UIDB/05757/2020 and Forest Alert Monitoring System (SAFe) Project through PROMOVE - Funda¸c˜ao La Caixa. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021, Thadeu Brito was supported by FCT PhD grant SFRH/BD/08598/2020

Seasonal approach to forecast the suitability of spawning habitats of a temperate small pelagic fish under a high-emission climate change scenario

ABSTRACT: Spawning habitats of cold-water, European small pelagic fishes have shifted poleward in the last three decades coincident with gradual ocean warming. We predicted present-day, season-specific habitat suitability for spawning by European sardine Sardina pichardus in the Atlantic Ocean and Mediterranean and Black Seas, and projected climate-driven changes in suitable areas from 2050-2099 under the IPCC – RCP 8.5 scenario. Sea surface temperature and distance to the coast had the greater influences in spawning habitats, reflecting the temperature- and coastal-dependent spawning of sardines. Chlorophyll-a was the third most important explanatory variable for spawning in winter to summer. Winds were predominantly important during autumn, whilst sea surface salinity was an important driver during spring and summer. Presentday, “hotspots” for spawning were identified in regions of highly productive, salty waters, where SST was between 6 and 18°C from autumn to spring or 16 and 25°C during summer and favourable winds occurred that would retain eggs and larvae closer to the coast (< 250 km). For future scenarios, forecasts indicate that environmental optima for spawning is projected to be in regions where SST varies between 11°C and 18°C from autumn to spring; and between 18°C and 24°C during summer. However, a negative relationship between phytoplankton productivity and habitat suitability induced by warming is likely to occur in the future. Projections suggest that suitable spawning habitats in all seasons will shift to higher latitudes, with a prominent range expansion along the coast of Norway during winter and autumn (> 83%). The total spawning area, however, was projected to contract in the future during spring (-10.5%) and autumn (-4.1%) due to losses of currently suitable areas along the Atlantic African Coast and Mediterranean Sea. Such regions currently support the greatest sardine stocks but climate-driven warming and decreased plankton productivity are projected to make these areas unsuitable for spawning and likely also for sardine fisheries in future.FEDER; Fundação para a Ciênica e Tecnologia - FCT; SARDITEMP; MATRIX; ARNET; SECILinfo:eu-repo/semantics/publishedVersio

Neurodevelopmental Syndrome with Intellectual Disability, Speech Impairment, and Quadrupedia Is Associated with Glutamate Receptor Delta 2 Gene Defect

Bipedalism, speech, and intellect are the most prominent traits that emerged in the evolution of; Homo sapiens; . Here, we describe a novel genetic cause of an "involution" phenotype in four patients, who are characterized by quadrupedal locomotion, intellectual impairment, the absence of speech, small stature, and hirsutism, observed in a consanguineous Brazilian family. Using whole-genome sequencing analysis and homozygous genetic mapping, we identified genes bearing homozygous genetic variants and found a homozygous 36.2 kb deletion in the gene of glutamate receptor delta 2 (; GRID2; ) in the patients, resulting in the lack of a coding region from the fifth to the seventh exons. The; GRID2; gene is highly expressed in the cerebellum cortex from prenatal development to adulthood, specifically in Purkinje neurons. Deletion in this gene leads to the loss of the alpha chain in the extracellular amino-terminal protein domain (ATD), essential in protein folding and transport from the endoplasmic reticulum (ER) to the cell surface. Then, we studied the evolutionary trajectories of the; GRID2; gene. There was no sign of strong selection of the highly conservative; GRID2; gene in ancient hominids (Neanderthals and Denisovans) or modern humans; however, according to in silico tests using the Mfold tool, the; GRID; 2 gene possibly gained human-specific mutations that increased the stability of; GRID2; mRNA

Impact of organizational factors on accident prediction in the retail sector

Although different actions to prevent accidents at work have been implemented in companies, the number of accidents at work continues to be a problem for companies and society. In this way, companies have explored alternative solutions that have improved other business factors, such as predictive analysis, an approach that is relatively new when applied to occupational safety. Nevertheless, most reviewed studies focus on the accident dataset, i.e., the casualty’s characteristics, the accidents’ details, and the resulting consequences. This study aims to predict the occurrence of accidents in the following month through different classification algorithms of Machine Learning, namely, Decision Tree, Random Forest, Gradient Boost Model, K-nearest Neighbor, and Naive Bayes, using only organizational information, such as demographic data, absenteeism rates, action plans, and preventive safety actions. Several forecasting models were developed to achieve the best performance and accuracy of the models, based on algorithms with and without the original datasets, balanced for the minority class and balanced considering the majority class. It was concluded that only with some organizational information about the company can it predict the occurrence of accidents in the month ahead.USDA - U.S. Department of Agriculture(PCIF/GRF/0141/2019)The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), ALGORITMI UIDB/00319/2020 and SusTEC (LA/P/0007/2021). This work has been supported by NORTE-01-0247-FEDER-072598 iSafety: Intelligent system for occupational safety and well-being in the retail sector. Inˆes Sena was supported by FCT PhD grant UI/BD/153348/2022