Abstract
This paper addresses a critical gap in applying semantic enrichment for online news text classification using large language models (LLMs) in fast-paced newsroom environments. While LLMs excel in static text classification tasks, they struggle in real-time scenarios where news topics and narratives evolve rapidly. The dynamic nature of news, with frequent introductions of new concepts and events, challenges pre-trained models, which often fail to adapt quickly to changes. Additionally, the potential of ontology-based semantic enrichment to enhance model adaptability in these contexts has been underexplored. To address these challenges, we propose a novel supervised news classification system that incorporates semantic enrichment to enhance real-time adaptability. This approach bridges the gap between static language models and the dynamic nature of modern newsrooms. The system operates on an adaptive prequential learning framework, continuously assessing model performance on incoming data streams to simulate real-time newsroom decision-making. It supports diverse content formats - text, images, audio, and video - and multiple languages, aligning with the demands of digital journalism. We explore three strategies for deploying LLMs in this dynamic environment: using pre-trained models directly, fine-tuning classifier layers while freezing the initial layers to accommodate new data, and continuously fine-tuning the entire model using real-time feedback combined with data selected based on specified criteria to enhance adaptability and learning over time. These approaches are evaluated incrementally as new data is introduced, reflecting real-time news cycles. Our findings demonstrate that ontology-based semantic enrichment consistently improves classification performance, enabling models to adapt effectively to emerging topics and evolving contexts. This study highlights the critical role of semantic enrichment, prequential evaluation, and continuous learning in building robust and adaptive news classification systems capable of thriving in the rapidly evolving digital news landscape. By augmenting news content with third-party ontology-based knowledge, our system provides deeper contextual understanding, enabling LLMs to navigate emerging topics and shifting narratives more effectively. Copyright © 2024 for this paper by its authors.

Abstract
Catharanthus roseus leaves produce a range of monoterpenoid indole alkaloids (MIAs) that include low levels of the anticancer drugs vinblastine and vincristine. The MIA pathway displays a complex architecture spanning different subcellular and cell type localizations, and is under complex regulation. As a result, the development of strategies to increase the levels of the anticancer MIAs has remained elusive. The pathway involves mesophyll specialized idioblasts where the late unsolved biosynthetic steps are thought to occur. Here, protoplasts of C. roseus leaf idioblasts were isolated by fluorescence-activated cell sorting, and their differential alkaloid and transcriptomic profiles were characterized. This involved the assembly of an improved C. roseus transcriptome from short- and long-read data, IDIO+. It was observed that C. roseus mesophyll idioblasts possess a distinctive transcriptomic profile associated with protection against biotic and abiotic stresses, and indicative that this cell type is a carbon sink, in contrast to surrounding mesophyll cells. Moreover, it is shown that idioblasts are a hotspot of alkaloid accumulation, suggesting that their transcriptome may hold the key to the in-depth understanding of the MIA pathway and the success of strategies leading to higher levels of the anticancer drugs. Catharanthus mesophyll idioblasts are a hotspot of alkaloid accumulation. The idioblast transcriptome is associated with stress responses and provides a roadmap towards the increase of anticancer alkaloid levels.

Abstract
MotivationAccurate determination and quantification of the taxonomic composition of microbial communities, especially at the species level, is one of the major issues in metagenomics. This is primarily due to the limitations of commonly used 16S rRNA reference databases, which either contain a lot of redundancy or a high percentage of sequences with missing taxonomic information. This may lead to erroneous identifications and, thus, to inaccurate conclusions regarding the ecological role and importance of those microorganisms in the ecosystem.ResultsThe current study presents MIMt, a new 16S rRNA database for archaea and bacteria's identification, encompassing 47 001 sequences, all precisely identified at species level. In addition, a MIMt2.0 version was created with only curated sequences from RefSeq Targeted loci with 32 086 sequences. MIMt aims to be updated twice a year to include all newly sequenced species. We evaluated MIMt against Greengenes, RDP, GTDB and SILVA in terms of sequence distribution and taxonomic assignments accuracy. Our results showed that MIMt contains less redundancy, and despite being 20 to 500 times smaller than existing databases, outperforms them in completeness and taxonomic accuracy, enabling more precise assignments at lower taxonomic ranks and thus, significantly improving species-level identification.

Abstract
Lack of timeliness and capacity are seen as fundamental problems that jeopardise healthcare delivery systems everywhere. Many believe the shortage of medical providers is causing this timeliness problem. This action research presents how one doctor implemented the theory of constraints (TOC) to improve the throughput (quantity of patients treated) of his ophthalmology imaging practice by 64% in a few weeks with little to no expense. The five focusing steps (5FS) guided the TOC implementation - which included the drum-buffer-rope scheduling and buffer management - and occurred in a matter of days. The implementation provided significant bottom-line results almost immediately. This article explains each step of the 5FS in general terms followed by specific applications to healthcare services, as well as the detailed use in this action research. Although TOC successfully addressed the practice problems, this implementation was not sustained after the TOC champion left the organisation. However, this drawback provided valuable knowledge. The article provides insightful knowledge to help readers implement TOC in their environments to provide immediate and significant results at little to no expense.

Abstract
PurposeTo develop, implement and assess the results of psychoeducation to improve the QoL of parents with CHD newborns.MethodsParticipants were parents of inpatient newborns with the diagnosis of non-syndromic CHD. We conducted a parallel RCT with an allocation ratio of 1:1 (intervention vs. control), considering the newborns, using mixed methods research. The intervention group received psychoeducation (Parental Psychoeducation in CHD [PPeCHD]) and the usual routines, and the control group received just the regular practices. The allocation concealment was assured. PI was involved in enrolling participants, developing and implementing the intervention, data collection and data analysis. We followed the Consolidated Standards of Reporting Trials (CONSORT) guidelines.ResultsParents of eight newborns were allocated to the intervention group (n = 15 parents) and eight to the control group (n = 13 parents). It was performed as an intention-to-treat (ITT) analysis. In M2 (4 weeks), the intervention group presented better QoL levels in the physical, psychological, and environmental domains of World Health Organization Quality of Life instrument (WHOQOL-Bref). In M3 (16 weeks), scores in physical and psychological domains maintained a statistically significant difference between the groups.ConclusionsThe PPeCHD, the psychoeducational intervention we developed, positively impacted parental QoL. These results support the initial hypothesis. This study is a fundamental milestone in this research field, adding new essential information to the literature.

Abstract
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.