Search

Abstract Background Biogenic nanoparticles possess a capping of biomolecules derived from the organism employed in the synthesis, which contributes to their stability and biological activity. These nanoparticles have been highlighted for the control of phytopathogens, so there is a need to understand their composition, mechanisms of action, and toxicity. This study aimed to investigate the importance of the capping and compare the effects of capped and uncapped biogenic silver nanoparticles synthesized using the filtrate of Trichoderma harzianum against the phytopathogenic fungus Sclerotinia sclerotiorum. Capping removal, investigation of the composition of the capping and physico-chemical characterization of the capped and uncapped nanoparticles were performed. The effects of the nanoparticles on S. sclerotiorum were evaluated in vitro. Cytotoxicity and genotoxicity of the nanoparticles on different cell lines and its effects on nontarget microorganisms were also investigated. Results The capped and uncapped nanoparticles showed spherical morphology, with greater diameter of the uncapped ones. Functional groups of biomolecules, protein bands and the hydrolytic enzymes NAGase, β-1,3-glucanase, chitinase and acid protease from T. harzianum were detected in the capping. The capped nanoparticles showed great inhibitory potential against S. sclerotiorum, while the uncapped nanoparticles were ineffective. There was no difference in cytotoxicity comparing capped and uncapped nanoparticles, however higher genotoxicity of the uncapped nanoparticles was observed towards the cell lines. Regarding the effects on nontarget microorganisms, in the minimal inhibitory concentration assay only the capped nanoparticles inhibited microorganisms of agricultural importance, while in the molecular analysis of the soil microbiota there were major changes in the soils exposed to the uncapped nanoparticles. Conclusions The results suggest that the capping played an important role in controlling nanoparticle size and contributed to the biological activity of the nanoparticles against S. sclerotiorum. This study opens perspectives for investigations concerning the application of these nanoparticles for the control of phytopathogens.

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.

In 2023, most parts of the world experienced exceptional heat. In particular, anomalous warm temperatures and heatwave events were evidenced across South America during the second half of the year. The situation was particularly critical in the Amazon region in terms of not only hydrometeorological drought but also ecological and socioeconomic impacts. In this study, remote-sensing data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to observe the changes in temperature and vegetation across Amazonia during the exceptional drought of 2023. This analysis was based on anomalies in the land surface temperature (LST) and vegetation indices: the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). The amplitude of the LST (AMP-LST), an indicator of the energy partitioning between the latent and sensible heat flux, and fire counts were also considered. The results show widespread and extreme warming across Amazonia during the austral spring in 2023, accompanied by a decline in vegetation greenness, water stress conditions across northern Amazonia, and an enhanced fire occurrence across central and northern Amazonia. [ABSTRACT FROM AUTHOR]

Copyright of Sigmae is the property of Universidade Federal de Alfenas (UNIFAL-MG) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Innovation can be a source of competitive advantage for companies, either through the improvement of methods and techniques capable of generating new products or services, or perfecting existing ones. Along these lines, it is particularly important to measure innovation and discuss the results associated therewith. The aim of this research is to address each of the main indicators of organizational innovation, separately discussing the advantages and disadvantages inherent to their deployment. The results entail a substantial theoretical advancement by improving measurement systems from various perspectives: products and processes, manufacturing and services, as well as input indicators, intermediaries and outputs. However, difficulties and limitations are still apparent, including the sort of distorting biases potentially affecting researcher estimates. Furthermore, multidimensional indicators provide a broader and more precise view of the innovation phenomenon at companies, because they are more comprehensive in the understanding of such phenomenon per se, unlike the one-dimensional choice, which carries constraints for discussing the real effects in the organizational context, especially in the case of services.

Despite extensive studies on the curve-shaped bacterium Vibrio cholerae, the causative agent of the diarrheal disease cholera, its virulence-associated regulatory two-component signal transduction system VarS/VarA is not well understood. This pathway, which mainly signals through the downstream protein CsrA, is highly conserved among gamma-proteobacteria, indicating there is likely a broader function of this system beyond virulence regulation. In this study, we investigated the VarA-CsrA signaling pathway and discovered a previously unrecognized link to the shape of the bacterium. We observed that varA-deficient V. cholerae cells showed an abnormal spherical morphology during late-stage growth. Through peptidoglycan (PG) composition analyses, we discovered that these mutant bacteria contained an increased content of disaccharide dipeptides and reduced peptide crosslinks, consistent with the atypical cellular shape. The spherical shape correlated with the CsrA-dependent overproduction of aspartate ammonia lyase (AspA) in varA mutant cells, which likely depleted the cellular aspartate pool; therefore, the synthesis of the PG precursor amino acid meso-diaminopimelic acid was impaired. Importantly, this phenotype, and the overall cell rounding, could be prevented by means of cell wall recycling. Collectively, our data provide new insights into how V. cholerae use the VarA-CsrA signaling system to adjust its morphology upon unidentified external cues in its environment.

Purpose: This study evaluates the role of social networks of support, information, and trust in well-being at work, regarding the positive and negative affects and professional fulfillment of workers immersed in processes of organizational change. Originality/value: Organizational change is characterized as a dynamic process, constituted through relationships, immersed in a context of uncertainties. The mapping of relationships can help in understanding the information flows and the assessment of resource availability. Design/methodology/approach: 151 professionals from a holding participated. This company undergoes changes in the scope of services offered and the organizational design. Links of support, information, and trust established according to the change processes were mapped. Associated with the network, the Well-Being at Work Scale was used. For data analysis, multiple regressions were used to construct explanatory models for well-being factors: fulfillment, positive and negative affects. Findings: Variables in support and information social network analysis (SNA) composed the predictive model of well-being in the three models. Among the researched ties, the support and information ties were part of the predictive model of well-being. The metrics that reveal how many times the employee is indicated and indicates others, proximity to highly cited neighbors, degree of participation in cohesive subgroups, the degree to which they assume a central position in the subgroups are indicators of actors’ positions capable of predicting well-being. The influence of the pattern of interaction between managers and employees should be considered in promoting well-being in organizations in the process of change. / Objetivo: Este estudo avalia o papel das redes de apoio, informação e confiança no bem-estar no trabalho quanto aos afetos positivos e negativos e à realização profissional de trabalhadores em processos de mu- dança organizacional. Originalidade/valor: A mudança organizacional é um processo dinâmico, constituído por meio das relações, em um contexto de incertezas. O ma- peamento das relações pode auxiliar na compreensão dos fluxos de informação e na avaliação da disponibilidade de recursos. Design/metodologia/abordagem: Participaram 151 profissionais de uma holding de transportes rodoviários que passa por mudanças no escopo dos serviços e no desenho organizacional. Mapearam-se laços de apoio, informação e confiança estabelecidos em função dos processos de mu- dança. Associada ao mapeamento, utilizou-se a Escala de Bem-Estar no Trabalho. Para análise, adotaram-se estatísticas descritivas e regressões múltiplas na construção de modelos explicativos para as dimensões de bem-estar no trabalho: realização e afetos positivos e negativos. Resultados: Variáveis em análise de redes sociais (ARS) de apoio e informação compuseram o modelo preditivo de bem-estar nos três modelos. Os laços de apoio e informação integraram o modelo preditivo de bem-estar. As métricas que revelam a quantidade de vezes que o colaborador é indicado e indica outros, a proximidade de vizinhos muito citados, a participação em subgrupos coesos e o grau em que assume posição central nestes são as posições dos atores capazes de predizer bem-estar. A influência do padrão de interação entre gestores e colaboradores deve ser considerada na promoção de bem-estar em organizações em processo de mudança. / Purpose: This study evaluates the role of social networks of support, information, and trust in well-being at work, regarding the positive and negative affects and professional fulfillment of workers immersed in processes of organizational change. Originality/value: Organizational change is characterized as a dynamic process, constituted through relationships, immersed in a context of uncertainties. The mapping of relationships can help in understanding the information flows and the assessment of resource availability. Design/methodology/approach: 151 professionals from a holding participated. This company undergoes changes in the scope of services offered and the organizational design. Links of support, information, and trust established according to the change processes were mapped. Associated with the network, the Well-Being at Work Scale was used. For data analysis, multiple regressions were used to construct explanatory models for well-being factors: fulfillment, positive and negative affects. Findings: Variables in support and information social network analysis (SNA) composed the predictive model of well-being in the three models. Among the researched ties, the support and information ties were part of the predictive model of well-being. The metrics that reveal how many times the employee is indicated and indicates others, proximity to highly cited neighbors, degree of participation in cohesive subgroups, the degree to which they assume a central position in the subgroups are indicators of actors’ positions capable of predicting well-being. The influence of the pattern of interaction between managers and employees should be considered in promoting well-being in organizations in the process of change.