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Impact of COVID-19 on health-related quality of life in students and PhD students: a systematic review
No full textHistorically, epidemics have always posed a global threat to humanity not only in terms of health and its domains – physical, social, and psychological – but also in all spheres of human existence, including the economic, social, political, ethnocultural, and religious. Regardless of their cause or route of spread, the measures taken to limit the epidemics that have occurred have always aimed to restrict human activities in different ways and to varying degrees. The aim of this study is to determine the impact of anti-epidemic measures against the COVID-19 pandemic on health-related quality of life in a target group – students and PhD students. Materials and methods: We conducted a systematic review of scientific publications in journals referenced and indexed in world-renowned databases using keywords – a total of 90. We applied the criteria system of the PICOS tool to assess outcomes. Results and discussion: We found a high frequency of anxiety, depressive symptoms, stress, insomnia, and reduced quality of life in the observed population of 13,704 students and PhD students. The negative impact of the pandemic on the mental health and quality of life of students and PhD students is complex and multifactorial. The importance of the university environment, not only as an academic space but also as a social and health space, can play a key role in building resilience among young people in future crisis situations
Review of Chinese species of the leafhopper genus Scaphomonoides Li, 2011 (Hemiptera, Cicadellidae, Deltocephalinae, Scaphoideini), with description of a new species
No full textThe leafhopper genus Scaphomonoides Li, 2011 is redescribed, and a new species Scaphomonoides robustus sp. nov. is described and illustrated from Yunnan Province, China. A key is given to distinguish the two species of this genus. The type specimen of the new species is deposited in the Institute of Entomology, Guizhou University, Guiyang, China (GUGC)
New geographic distribution records of Liometopum apiculatum Mayr, 1870 (Hymenoptera, Formicidae) in central northern Mexico
No full textThe geographic distribution of Liometopum apiculatum Mayr, 1870 is expanded and updated, based on specimens collected in the regions of the Mexican Plateau, Sierra Madre Oriental, and Sierra Madre Occidental. Ten new records include the first municipal occurrences for the states of Zacatecas (Guadalupe, Sombrerete, Valparaíso, and Zacatecas) and San Luis Potosí (Mexquitic de Carmona), and additional locality records for Nuevo León (Galeana and Iturbide) and Coahuila (Saltillo)
Lepidoptera of North America, north of Mexico: an annotated list containing geographic ranges and host-plant records
No full textWe provide a list of all named Lepidoptera in the USA and Canada. Data include ranges, host plants, and synonymies. Information is annotated, including detailed range notes and host-plant records. Data are also provided in unique, easily filtered fields. The list establishes 12,541 native species, 325 exotic species, and 189 species straying occasionally into North America. In addition, we list 146 known but undescribed species, 112 species with uncertain or unresolved status, and 450 excluded species. 7423 of the described species in North America have host record data. Information is presented here as an Excel spreadsheet
The Canadian Genomic Adaptation and Resilience to Climate Change (GenARCC) Project
No full textGenomic technologies provide the highest resolution molecular information on species biology, and can help us understand risks and potential for adaptation among species in a changing environment. The Genomic Adaptation and Resilience to Climate Change (GenARCC) project uses molecular tools to identify ecosystem composition, pathogen and pest prevalence, and adaptive capacity within species. This Government of Canada project purposefully takes a multi-department/agency approach, drawing on complementary expertise and centralized infrastructure to address the complexity of climate change spanning multiple levels of trophic and taxonomic diversity. The GenARCC project’s goal is to develop capacity to use genomics to assess, predict, and adapt to climate change for the protection of Canada's biodiversity, ecosystem resilience, food security, and health. Together, the data and expertise generated in this project represent the largest single national effort and combined dataset to address climate change impacts across species and ecosystems with molecular data. GenARCC comprises research across a diversity of environments and species, covering forest and tundra, arctic, marine, and agricultural ecosystems. Genomic, climate and phenomic datasets have been generated and centralized to better understand impacts to biodiversity across biological levels of organisation. To facilitate storage and analysis of this data, we have deployed a common high performance compute environment allowing participants across multiple Canadian departments and agencies to leverage shared infrastructure, datasets and workflows. Project outputs and inputs are managed through a dedicated sample data research management platform, DINA (Fig. 1). DINA utilizes a highly flexible data model with a core set of fields that can be extended using domain-based standards as field-extensions and/or managed attributes that are user defined, with both able to leverage controlled vocabularies. Strong process-based provenance is maintained for samples and their derivatives. Samples managed, for example, range from individual specimens of bacteria and viruses, plants, insects, fish, and mammals to environmental samples of soil, water, and external and internal microbiomes. A robust API facilitates data migration and customized export supporting data analysis and publication (GitHub Repo). The project has produced several standardized pipelines using Snakemake and Nextflow for efficiency and ease of comparison. An effort was also made to standardize Canadian climatic data for modelling allowing for cross-species comparisons (Marquis 2024).Additionally, project participants benefit from training on scientific computing, as well as genomic methods in discussion forums for data analysis and integration best practices. This access to computing resources and training has supported publication of more than 30 studies that can be found on the GenARCC publication website. Results from this project will inform evidence-based policy to support conservation of biodiversity, as well as management of natural resources and key species across ecological realms
Hidden in the Data: Unlocking the Potential of Brazil’s Sociobiodiversity for a Sustainable Bioeconomy
No full textBrazil harbors the richest biodiversity on Earth, accounting for 15–20% of all known species worldwide (Lewinsohn 2006). Its territory also encompasses significant cultural diversity, represented by 355 officially recognized Indigenous lands, 253 Afro-descendent territories, and 28 groups of traditional communities such as rubber tappers, coconut breakers and veredeiros (de Mendonça 2009). Despite this potential, the economy remains anchored in an agro-export model based on exotic commodity crops – most notably soybeans, strongly associated with deforestation and rising inequalities (Lobão and Staduto 2020). In contrast, Brazil’s sociobiodiversity, the combination of socio-cultural and biological richness, underpins the potential for a sociobiodiversity-based bioeconomy (Garrett et al. 2024), an approach that integrates traditional knowledge, sustainable use of native species, and innovation into broader economic agendas. Yet, the potential for developing this bioeconomy remains constrained. A central barrier is the invisibility of sociobiodiversity in official statistics.To examine how sociobiodiversity products and local markets are represented in official datasets, we conducted a local-scale study in Northern Minas Gerais, within the Cerrado biome – a global biodiversity hotspot increasingly threatened by agribusiness-driven deforestation. We compared production data from the Brazilian Institute of Geography and Statistics (IBGE) and subsidy data from the National Supply Company (Conab) with figures provided by the Cooperative Grande Sertão, a leading local cooperative that works directly with 400 families across nearly 40 municipalities. IBGE is the official agency responsible for compiling national statistics on the production of non-timber forest products such as açaí (Euterpe oleracea), Brazil nut (Bertholletia excelsa) and natural rubber (Hevea brasiliensis), among others, and Conab is the governmental company that monitors products eligible for public procurement. Our results revealed two main key messages. First, value chains of native species that are socially and economically central to traditional communities are largely absent from systematic monitoring regarding their use, production, or market flows, corroborating previous studies (Porro 2019, Carvalho Ribeiro et al. 2024), and excluded from public policies. Of the 18 native species collected and commercialized by agroextractivists locally, only four (umbu – Spondias tuberosa, mangaba – Hancornia speciosa, pequi – Caryocar brasiliensis, and buriti – Mauritia flexuosa) are tracked in official datasets. Only five species, including macaúba (Acrocomia aculeata), are recognized under public procurement programs, despite their socio-economic relevance. Even for pequi, the most consolidated product, official data is incomplete (Fig. 1). Among the 1,434 municipalities in the Cerrado, its natural distribution area, only 292 (20%) appear in IBGE statistics, as the institute only records volumes above one ton. This implies that in 42% of municipalities, pequi production exists but remains unregistered, while in 38% there is no data at all.Our second finding was the marked discrepancy between official statistics and actual production reported by cooperatives. In four leading pequi-producing municipalities, official records diverged substantially from local data. In Cônego Marinho, for instance, just 30 agroextractivists collected six times more pequi than IBGE registered for the entire municipality in 2023, and in Miravânia, two collectors harvested more than five times the official figure (Fig. 1). These discrepancies may arise from multiple factors, including the informal or subsistence character of many extractive activities. As a result, the national statistics may fail to capture the full economic, ecological, and cultural importance of native species to rural and traditional communities. Understanding these data mismatches is essential for improving the accuracy and inclusiveness of biodiversity and bioeconomy indicators.Our study underscores the need to strengthen local monitoring capacities and create integrated data systems that combine official records with community-based knowledge to unlock the transformative potential of Brazil’s sociobiodiversity. Investments in territorialized data governance are essential to ensure more accurate representation of sociobiodiversity value chains, enabling place-based policies that align biodiversity conservation with inclusive economic development. Strengthening the visibility of native species in data systems is needed not only to improve accuracy but also to ensure informed decision-making, equitable benefit-sharing, and resilience in the face of environmental and social challenges. Making sociobiodiversity visible in data is a prerequisite for building a sustainable and just bioeconomy
Case of severe preeclampsia
No full textWe present a case of a 25-year-old primiparous woman with eclampsia imminens and hemolysis, elevated liver enzymes, and low platelet count (HELLP) syndrome, requiring a cesarean section. Through efficient collaboration between obstetricians, neonatologists, cardiologists, resuscitators and neurologists, a successful therapeutic outcome was achieved for both the mother and the newborn
Editorial
No full textDear Readers,It gives me great pleasure to announce the 13th J.UCS issue of 2025. I would like to thank all the authors for their sound research and the editorial board and guest reviewers for the extremely valuable reviews and suggestions for improvement. These contributions together with the support of the community and the generous support of the KOALA initiative enable us to run our journal and maintain its quality.I would still like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and emerging trends.In this regular issue, I am very pleased to present 6 accepted articles by 18 authors from 9 countries: Australia, Austria, Brazil, Chile, China, Iran, Malaysia, Spain, and Turkiye.In a collaborative effort between researchers from Australia and Austria, Muhammad Saqlain, José M. Merigó, Keivan Amirbagheri, and Hermann Maurer provide a comprehensive bibliometric analysis of the Journal of Universal Computer Science (JUCS) from 1994 to 2024, employing the SPAR-4-SLR protocol with VOSviewer and Bibliometrix to examine publication patterns, collaboration networks, and thematic evolution. The findings reveal JUCS’s strengthened global impact, intellectual connectivity, and transition toward modern computer science domains such as artificial intelligence, deep learning, and big data, underscoring its sustained relevance and adaptability over three decades.Claudio Alvarez, Andres Carvallo, and Gustavo Zurita from Chile address in their research the growing orchestration load teachers face in real-time case-based learning discussions by introducing a low-footprint natural language processing approach that can run on standard hardware without requiring large-scale models. Expert evaluation shows that small pre-trained models, particularly BETO and the Universal Sentence Encoder, effectively identify relevant student responses while maintaining low computational cost and minimizing bias, enabling scalable and equitable AI support for educators in the Global South.In a collaborative research effort between Malaysia and China, Yongbin Li, Xinyue Yang, Linhu Hui, Enlin Fu, and Stephanie Chua focus on Lung Nodule Detection. To reduce false positives on CT scans, the authors propose AMCF-CNN, a 3D attention-guided multi-scale cross-fusion network that effectively integrates local features and global contextual information through SimAM-Res and the Global Modeling Module. Evaluated on the LUNA16 dataset, AMCF-CNN achieves a CPM of 0.936 and a balanced accuracy of 0.983, outperforming most existing methods. José de Oliveira Guimarães from Brazil focuses his research on aspects of metaprogramming in Cyan. Most compile-time metaprogramming languages allow unrestricted modifications to the in-memory representation of the base program by providing largely unconstrained access to the compiler’s internal data structures. The Cyan metaprogramming system, in contrast, uses a sandboxed model that prevents errors caused by such unrestricted access while retaining most of the expressive power of other systems.Vahide Nida Kılıç and Esra Saraç Eşsiz from Turkiye propose in their research a novel anomaly prevention framework that combines clustering-based nature-inspired algorithms with both node and content features to identify suspicious instances in communication networks. The approach advances the field by shifting from traditional anomaly detection to proactive prevention through the early classification of suspicious nodes, threshold-based risk assessment, and link analysis to flag potential anomalies before escalation.Atefeh Parvin, Farahnaz Mohanna, and Masoumeh Rezaei from Iran discuss in their research a genetic-based square jigsaw puzzle solver. To address the challenge of distinguishing identically colored pieces from different objects in jigsaw puzzle solving, they propose a genetic algorithm-based solver that integrates a novel color and texture compatibility criterion using Sum of Squared Distances and Gabor filter features. This approach improves accuracy by 11.9% and 3.65% in direct and neighbor comparison criteria across 66 puzzles, offering a data-efficient solution for type 1, 2, and 3 square jigsaw puzzles.Enjoy Reading!Cordially, Christian Gütl, Managing Editor-in-ChiefGraz University of Technology, Graz, Austri
Thismia selangorensis (Thismiaceae): a new mitriform fairy lantern species from Selangor, Malaysia
No full textThismia selangorensis, a distinct mitriform species of the mycoheterotrophic genus Thismia, is described herein. It was first discovered in a tree hole on a riverbank in Taman Eko Rimba Sungai Chongkak, Selangor, Malaysia. This new species is superficially similar to members of Thismia section Geomitra in that it has coralliform roots, inner tepals forming a mitre with three appendages on top, and stamens with a prominent dorsal rib. However, T. selangorensis differs from known species of T. sect. Geomitra in several morphological features, including the colour of the flowers, the shape of the mitre, the shape of the inner tepal lobes forming the mitre, and the presence of translucent reticulation on the inner surface of the floral tube. Thismia selangorensis is provisionally classified as Critically Endangered according to the IUCN Red List categories and criteria
Image-based recognition using advanced neural networks can aid surveillance of Agrilus jewel beetles
No full textThe genus Agrilus includes two species, Agrilus planipennis and A. anxius, that are of particular phytosanitary concern and that are regulated by the European Union legislation. This implies that phytosanitary agencies of all EU countries are obliged to establish specific surveillance programs to verify the absence of these species from their territory. These activities commonly consist of the use of green-colored traps, which are however attractive not only for A. planipennis and A. anxius, but also for a wide range of other Agrilus species. For this reason, much time and expertise is required to sort and identify specimens to species, impeding an efficient rapid response. In this study, we tested the efficacy of the Entomoscope, a low-cost, open-source photomicroscope that uses high-resolution digital imaging and allows a pre-trained Convolutional Neural Networks (CNN) model to accurately detect, image and classify insect specimens, for automatic identification of 13 Agrilus species, including A. planipennis and A. anxius. We benchmarked models from three different CNN architectures and selected YOLOv8l as the most robust performer; this model achieved a Top-1 accuracy of 90.2% on a “real-world” test set (i.e. a dataset simulating real surveillance conditions). For most species, including A. planipennis and A. anxius, either no errors or only a few errors were made, whereas for a few native species misidentifications were more common. These results provided proof of concept for an AI-driven surveillance system that can strongly aid in surveillance activities of Agrilus species