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The accumulation of microplastics (MPs) in the environment has been a bottleneck for scientific society. Several approaches have been described as possibilities for reducing MPs in aquatic and terrestrial ecosystems; however, most of them are not environmentally friendly. Filamentous fungi (Ff) cells are currently considered a promising solution as a treatment for MPs. Therefore, the present study reports the potential ability of Ff isolated from mangrove sediments to biodegrade low-density polyethylene MPs (LDPEMPs). Six Ff strains were grown in batch cultures for 28 days, and one of them, Aspergillus sp. (AQ3A), showed the most prominent profile to biodegrade polymeric compounds. After morphological and molecular analysis, all strains were identified as belonging to the genera Aspergillus (MQ1C, AQ2A and AQ3A), Penicillium (MQ1A), and Trichoderma (MQ1B and MQ2A). The strain Aspergillus sp. (AQ3A) showed the most promising results with a LDPEMPs reduction rate of 47% and biomass formation of 0.0890 g·mL−1. Complementary studies with Aspergillus sp. (AQ3A) using Fourier-transform infrared spectroscopy (FTIR) highlighted changes in the molecular structure of LDPEMPs. These results indicate that Ff can contribute to the biodegradation of LDPEMPs. However, other parameters, mainly associated with the enzymes that are involved in this biodegradation process, need to be explored. [ABSTRACT FROM AUTHOR] more...

Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC. [ABSTRACT FROM AUTHOR] more...

This article aims to present the confluence of academic aspects and religious interests in the first decades of the Study of Religion discipline in Brazil. The work seeks to highlight the political-academic and religious interrelationships that took place during this period of the discipline's establishment in the country. Methodologically, the text is based on sources such as minutes, course plans, opinions, memoirs, interviews and publications. The conclusion is that, under the confluence of academic interests and religious-pastoral aspirations, the Study of Religion discipline emerged on the national academic scene in the second half of the 20th century with little self-understanding of its theoretical-methodological profile. [ABSTRACT FROM AUTHOR] more...

Natural computing is a terminology used to describe computational algorithms developed by taking inspiration from information processing mechanisms in nature, methods to synthesize natural phenomena in computers, and novel computational approaches based on natural materials. The virtual laboratory on natural computing (LVCoN) is a Web environment to support the teaching and learning of natural computing, and whose goal is to provide didactic contents about the main themes in natural computing, in addition to interactive simulations, videos, exercises, links for related sites, forum, and other materials. This article describes an experiment with LVCoN during a School of Computing in Brazil. The results are presented in four parts: Self-Evaluation, Evaluation of LVCoN, Evaluation of the Simulations (Applets), and Interviews. The results allowed us to positively evaluate the structure and contents of LVCoN, in the sense that most students were satisfied with the environment. Besides, most students liked the experience of working with a virtual laboratory, and considered a hybrid teaching approach; that is, one mixing lectures with virtual learning, very appropriate and productive. more...

Extracting knowledge from text data is a complex task that is usually performed by first structuring the texts and then applying machine learning algorithms, or by using specific deep architectures capable of dealing directly with the raw text data. The traditional approach to structure texts is called Bag of Words (BoW) and consists of transforming each word in a document into a dimension (variable) in the structured data. Another approach uses grammatical classes to categorize the words and, thus, limit the dimension of the structured data to the number of grammatical categories. Another form of structuring text data for analysis is by using a distributed representation of words, sentences, or documents with methods like Word2Vec, Doc2Vec, and SBERT. This paper investigates four classes of text structuring methods to prepare documents for being clustered by an artificial immune system called aiNet. The goal is to assess the influence of each structuring method in the quality of the clustering obtained by the system and how methods that belong to the same type of representation differ from each other, for example both LIWC and MRC are considered grammarbased models but each one of them uses completely different dictionaries to generate its representation. By using internal clustering measures, our results showed that vector space models, on average, presented the best results for the datasets chosen, followed closely by the state of the art SBERT model, and MRC had the overall worst performance. We could also observe a consistency in the number of clusters generated by each representation and for each dataset, having SBERT as the model that presented a number of clusters closer to the original number of classes in the data. [ABSTRACT FROM AUTHOR] more...

Pyoderma gangrenosum is a rare neutrophilic disease of unknown origin that is associated with systemic diseases in 50% of cases. It is characterized by erythematous-violaceous nodular lesions that quickly progress to painful ulcers, with undermined edges, necrotic-hemorrhagic, varying in size and depth, located mainly in the lower limbs. Extracutaneous locations of pyoderma gangrenosum are rare, usually involving the lungs; the main differential diagnosis in these cases is Wegener granulomatosis. We report a case of pyoderma gangrenosum, which showed multiple cavitary lung nodules, with good response to high doses of steroids. Once excluded the possibility of Wegener granulomatosis, the authors concluded that it was the manifestation of systemic pyoderma gangrenosum with pulmonary involvement.Pioderma gangrenoso é doença neutrofílica rara de etiologia desconhecida, que se associa a doenças sistêmicas em 50% dos casos. Caracteriza-se clinicamente por lesão nodular eritematoviolácea ou pústula que progride rapidamente para úlcera dolorosa, de bordas irregulares, fundo necrohemorrágico e localização preferencial nos membros inferiores. Manifestações sistêmicas do Pioderma gangrenoso são raras, envolvem geralmente os pulmões e o principal diagnóstico diferencial nestes casos é a granulomatose de Wegener. Relatamos um caso de paciente portador de pioderma gangrenoso que apresentava múltiplos nódulos pulmonares cavitados, com boa resposta a altas doses de corticoterapia. Uma vez excluída a possibilidade de granulomatose de Wegener, os autores concluíram tratar-se da manifestação sistêmica do pioderma gangrenoso. more...

Siparuna brasiliensis is a medicinal plant widely used by indigenous communities of the Amazon rainforest to treat inflammatory diseases and related pathologies. Considering its ethnopharmacological application, it constitutes an important source of biologically active molecules in the development of anti-inflammatory drugs. This study describes a dereplication methodology of the bioactive extract from S. brasiliensis leaves and the evaluation of the anti-inflammatory potential in an in vivo inflammatory model with mice of the BALB/c lineage and in vitro using cell lines, as well as determining the production of an inflammatory mediator. From their charge-to-mass ratios (m/z) and elemental composition obtained through Ultrahigh-resolution mass spectrometry analysis by ESI(-)-Orbitrap MS and chromatographic profile by RP-HPLC-PDA, it was possible to annotate polyphenols with anti-inflammatory properties classified as flavonoids and organic acids. The administration of the extract significantly inhibited carrageenan-induced paw edema and showed effects similar to those of drug dexamethasone without affecting cell viability. [ABSTRACT FROM AUTHOR] more...

The seahorse is a marine teleost fish member of the Syngnathidae family that displays a complex variety of morphological and reproductive behavior innovations and has been recognized for its medicinal importance. In the Brazilian ichthyofauna, the seahorse Hippocampus reidi is among the three fish species most used by the population in traditional medicine. In this study, a protocol was performed based on fast heat inactivation of proteases plus liquid chromatography coupled to mass spectrometry to identify native peptides in gills of seahorse H. reidi. The MS/MS spectra obtained from gills allowed the identification of 1080 peptides, of which 1013 peptides were present in all samples and 67 peptide sequences were identified in an additional LC-MS/MS run from an alkylated and reduced pool of samples. The majority of peptides were fragments of the internal region of the amino acid sequence of the precursor proteins (67%), and N- and C-terminal represented 18% and 15%, respectively. Many peptide sequences presented ribosomal proteins, histones and hemoglobin as precursor proteins. In addition, peptide fragments from moronecidin-like protein, described with antimicrobial activity, were found in all gill samples of H. reidi. The identified sequences may reveal new bioactive peptides. [ABSTRACT FROM AUTHOR] more...

Sea anemones are sessile invertebrates of the phylum Cnidaria and their survival and evolutive success are highly related to the ability to produce and quickly inoculate venom, with the presence of potent toxins. In this study, a multi-omics approach was applied to characterize the protein composition of the tentacles and mucus of Bunodosoma caissarum, a species of sea anemone from the Brazilian coast. The tentacles transcriptome resulted in 23,444 annotated genes, of which 1% showed similarity with toxins or proteins related to toxin activity. In the proteome analysis, 430 polypeptides were consistently identified: 316 of them were more abundant in the tentacles while 114 were enriched in the mucus. Tentacle proteins were mostly enzymes, followed by DNA- and RNA-associated proteins, while in the mucus most proteins were toxins. In addition, peptidomics allowed the identification of large and small fragments of mature toxins, neuropeptides, and intracellular peptides. In conclusion, integrated omics identified previously unknown or uncharacterized genes in addition to 23 toxin-like proteins of therapeutic potential, improving the understanding of tentacle and mucus composition of sea anemones. [ABSTRACT FROM AUTHOR] more...

Remote Laboratories (RLs) are software and hardware tools that allow students to remotely perform real experiments by means of an online system or platform. They represent an evolution in the learning process by making the execution of real-world experiments accessible for many students at distance. Learning Analytics (LAs), by contrast, is the research area concerned with the collection, measurement, analysis and reporting of data associated with learning and its outcomes. The application of LA to RLs leads to a better understanding and planning of the main teaching and learning processes, and their outcomes. This paper aims at providing a systematic review of the application of learning analytics to remote laboratories, thus building an up-to-date body of knowledge for researchers and professionals interested in the application of digital technologies into the educational context. This research follows a procedure based on three main steps: planning, conducting, andreporting. We searched seven STEM ( Science, Technology, Engineering and Mathematics) databases with two search queries. The retrieved documents were analyzed under the umbrella of five research questions, and a comprehensive organization and structure of the surveyed literature was proposed. The results obtained showed eight main RL platforms/systems (Netiab, WebLab-Deusto, Go- Lab, FORGE, Lab4CE, GOLDI, and MOOLs-based), and four categories of data to be retrieved and analyzed by LA methods. Also, we identified five types of metrics usually used to measure the outcome of the learning process, and five learning outcomes. This paper provided an up-to-date systematic review on the use of learning analytics within the remote lab context. We explored platforms, use cases, data retrieved, performance metrics, analysis methods, and learning outcomes. Among the many conclusions, it is possible to stress that the application of LA to RLs aids in the visualization of the learner's strengths and difficulties during aRL experiment, the automatic evaluation of the experiment, and efficient feedback. [ABSTRACT FROM AUTHOR] more...