Animo Repository - De La Salle University Research
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Conservation assessment and predictive distribution modeling of Philippine Rafflesia R. Br. ex Gray (Rafflesiaceae)
No full textThe genus Rafflesia comprises holoparasitic plants known for producing the world’s largest flowers and exhibiting extreme endemism, particularly among the 15 species recorded in the Philippines. Despite their ecological and biological complexity, 14 of these species remain unassessed by the IUCN. Although previous studies have conducted distribution modeling for selected Rafflesia species, these efforts were limited by the narrow range of environmental variables and geographic scope considered. This study is the first to assess the predictive habitat suitability of all Philippine Rafflesia species at the national level. MaxEnt modeling was employed to predict the potential geographic distribution of Rafflesia using occurrence data gathered from herbarium collections and citizen science records, in combination with 21 environmental factors and the protected area boundaries of the Philippines. The mapping of occurrence points revealed a high degree of endemism among most species, resulting in low Extent of Occurrence (EOO) and Area of Occupancy (AOO), which served as the basis for their proposed IUCN Red List categorization. These occurrence patterns were also compared against existing protected areas to evaluate conservation coverage. Based on IUCN criteria, six out of fifteen species are suggested to be categorized as Endangered, while eight are proposed to be listed as Critically Endangered. The MaxEnt model revealed that most Rafflesia occurrence points fall within highly suitable areas, while some suitable regions lack records, suggesting potential sites for future habitation. These findings aim to support future conservation efforts by identifying areas with high habitat suitability for Rafflesia and addressing knowledge gaps in species distribution and threat assessment
Regulating Generative AI in Scholarly Works: A Policy Brief for Academic Institutions in the Philippines
Get PDFThis policy brief aims to provide a comprehensive examination of the regulation of generative artificial intelligence (AI) in academic works, with a primary objective of establishing best practices and strategies for academic institutions, government entities, and civil society in emerging economies, notably the Philippines. It entails a review of the existing regulatory frameworks in various governments and academic institutions while also addressing challenges faced in regulating generative AI, particularly focusing on the intellectual property aspects related to the use of generative AI tools, such as ChatGPT, in academic research content or scholarly works output from such use of tools. The scope encompasses regulations in both developed nations such as Europe, the U.S., and China and developing countries, including Association of Southeast Asian Nations (ASEAN) members, with a specific emphasis on the Philippine context. In conclusion, this study underscores the transformative potential of generative AI in education, leading to varied regulatory approaches in academic institutions. While some universities have established provisional guidelines for responsible AI use, national governments are still crafting comprehensive policies to navigate the disruptive technology landscape. As a result of this study, we have developed proposals for each stakeholder, providing guidelines and recommendations for academic institutions, government entities, and civil society to create robust regulatory frameworks that foster responsible and innovative AI utilization in academia, address intellectual property concerns, especially on generative AI assistance, and provide clarity on AI technology deployment within educational settings.https://animorepository.dlsu.edu.ph/apipmibookseries/1006/thumbnail.jp
Assessment of the Quorum-Sensing interference of ficus septica “hauili” in a clinical isolate of extended-spectrum B-lactamase-producing Escherichia coli
No full textAntimicrobial resistance (AMR) continues to rise globally due to the widespread and inappropriate use of antimicrobials. Quorum-sensing (QS) inhibition, which disrupts bacterial QS without exerting positive selective pressure for resistant strains to survive and be disseminated, offers a promising alternative. This study investigated the QS-modulating effects of the ethanolic Ficus septica leaf extract on extended-spectrum β-lactamase (ESBL)-producing Escherichia coli PGH168 isolate. The QS inhibition effect of the extract was first tested on Chromobacterium violaceum ATCC 12472, a model for the detection of QS interference activities. The extract did not inhibit the violacein pigment in C. violaceum, indicating no effect on acyl-homoserine lactone (AHL)-based QS. In E. coli PGH168, the extract inhibited both swimming and swarming motility, but enhanced biofilm formation, a combination characteristic of AI-2 (autoinducer 2)-mediated QS activation during biofilm maturation. These phenotypes align with flhDC repression and csgD activation, promoting curli fiber production and the transition to a sessile lifestyle, a characteristic of biofilm formation. This suggests that the extract acts not as a QS inhibitor, but as an AI-2 agonist through mimicry of the native AI-2 signal. AI-2 mimicry occurs when non-bacterial molecules activate the LuxS/Lsr system by resembling or functionally substituting for AI-2. Phytochemicals in F. septica may therefore mimic AI-2, triggering QS responses that shift E. coli PGH168 toward biofilm formation. These results contrast with previous studies reporting anti-biofilm activity in other Ficus species, likely due to species-specific phytochemicals or context-dependent QS interactions. In conclusion, data showed that F. septica extract induced a shift in E. coli PGH168 from motility to a sessile, biofilm-forming state, via AI-2 pathway activation, highlighting the potential role of plant-derived compounds as QS agonists in a context-dependent manner. This study presents the first documented evidence of the QS-inducing activity of F. septica on ESBL-producing E. coli
Functional genomic insights into the recently resurrected Noah’s giant clam tridacna noae röding, 1798 (Cardiidae: Tridacninae): Key genes for immunity and environmental adaptation an in silico study
No full textTridacna noae, a symbiotic giant clam vital to coral reef ecosystems, remains genomically understudied, with no species-specific reference genome available to date. This limits molecular investigations into its physiology, resilience, and ecological role. To address this, we performed the first functional annotation of Tridacna noae using publicly available whole-genome sequencing data. Raw reads were quality-filtered and assembled de novo; gene prediction was carried out using AUGUSTUS, followed by functional annotation via BLASTp, Gene Ontology (GO), InterProScan, KEGG, and eggNOG-mapper. A total of 5,628 protein-coding genes were identified, with enriched functions in oxidative stress response, immune signaling, and symbiosis regulation. Partial representation of the cysteine and methionine metabolism pathway suggested potential for glutathione-based redox buffering. Immune-related genes—including TRIM71-like E3 ligases and beta-arrestins—were also detected, indicating mechanisms for pathogen defense and host-symbiont modulation. BLASTp results revealed significant alignment with mollusks, Symbiodinium spp., and sulfur-oxidizing bacteria, reflecting a complex holobiont structure. These findings underscore the need for de novo genome sequencing and transcriptomic validation to support further functional, ecological, and conservation research on Tridacna noae