Fungi from dead arthropods and bats of Gomantong cave, northern Borneo, Sabah (Malaysia)

Borneo is a biodiversity and ecotourism hotspot, yet one of its least-studied ecosystems is their limestone caves. Not many studies have been conducted on the role fungi play in tropical cave ecosystems, and no fungal surveys have been conducted in the caves of Sabah, Malaysia. Here, we assess the mycofloral diversity on bat and arthropod cadavers in one of the most popular ecotourism destinations of northern Borneo, Gomantong caves. Opportunistic sampling of cadavers within the Semud Hitam chamber of Gomantong cave yielded nine dead arthropods and four dead bats. Twenty-four culturable fungi were isolated, of which 14 morphological taxonomic units (MTU) were observed. Twelve of the 14 MTUs underwent molecular characterization of the ITS gene region to confirm identification. All fungi were Ascomycetes except for one Basidiomycete isolate. Aspergillus spp. had the highest occurrence (45.8%), followed by Penicillium spp. (25.0%), and Fusarium sp. (12.5%). Ceratobasidium sp., Diaporthe sp., Pestalotiopsis sp., and Xylaria feejeensis were isolated once each. No more than one fungal taxon was isolated from each arthropod cadaver, and not all arthropods yielded culturable fungi. Bat cadavers yielded 14 out of 24 isolates (58.3%), with the highest occurrence of the fungi sampled from their skin. Our results corroborate that bats and arthropods play a role in fungal dispersion and introduction in the cave because their exteriors are likely to harbor fungi they are exposed to in the environment. We also conclude that cadavers are important substrates for fungal growth and proliferation, perpetuating the role of fungi as important decomposers in caves. This study provides a baseline of information of the mycobiome of Bornean caves for future bioprospecting and potential biotechnological applications

Effect of Particle Size on Mechanical Properties of Sawdust-High Density Polyethylene Composites under Various Strain Rates

There is a need to understand the effect of wood particle size, as it affects the characteristics of wood-based composites. This study considers the effect of wood particle size relative to the dynamic behavior of wood composites. The compression Split Hopkinson Pressure Bar (SHPB) was introduced to execute dynamic compression testing at the strain rate of 650 s-1, 900 s-1, and 1100 s-1, whereas a conventional universal testing machine (UTM) was used to perform static compression testing at the strain rate of 0.1 s-1, 0.01 s-1, and 0.001 s-1 for four different particle sizes (63 µm, 125 µm, 250 µm, and 500 µm). The results showed that mechanical properties of composites were positively affected by the particle sizes, where the smallest particle size gave the highest values compared to the others. Moreover, the particle size also affected the rate sensitivity and the thermal activation volume of sawdust/HDPE, where smaller particles resulted in lower rate sensitivity. For the post-damage analysis, the applied strain rates influenced deformation behavior differently for all particle sizes of the specimens. In a fractographic analysis under dynamic loading, the composites with large particles experienced severe catastrophic deformation and damages compared to the smaller particles

Fungal communities in bat guano, speleothem surfaces, and cavern water in Madai cave, Northern Borneo (Malaysia)

The island of Borneo is a global biodiversity hotspot. However, its limestone caves are one of its least-studied ecosystems. We report for the first time the fungal species richness, diversity and abundance from Madai cave, situated in north-eastern Borneo. Environmental samples from inside the cave environment were collected (guano, speleothem, and cavern water) via opportunistic sampling. The dilution method was performed for isolation of fungi. Morphological characterisation and molecular analysis of the ITS region were utilised for the identification of isolates. Fifty-five pure cultures of fungi were attained, comprising 32 species from 15 genera, eight orders, and two divisions, Ascomycota and Basidiomycota. Ascomycetes dominated the fungal composition, accounting for 53 (96%) out of 55 total isolates. Penicillium spp. accounted for more than 47.1% of fungal abundance in all sample types. However, Aspergillus spp. had the highest occurrence rate, being isolated from all environmental samples except one. Purpureocillium lilacinum was isolated most frequently, appearing in five separate samples across all three substrates. Annulohypoxylon nitens, Ganoderma australe, Pyrrhoderma noxium, and Xylaria feejeensis were discovered and reported for the first time from the cave environment. This study provides additional data for further research on the mycoflora of Sabah’s various ecosystems, especially limestone caves

Optimization of Injection Moulding Process via Design of Experiment (DOE) Method based on Rice Husk (RH) Reinforced Low Density Polyethylene (LDPE) Composite Properties

Optimal parameters setting of injection moulding (IM) machine critically effects productivity, quality, and cost production of end products in manufacturing industries. Previously, trial and error method were the most common method for the production engineers to meet the optimal process injection moulding parameter setting. Inappropriate injection moulding machine parameter settings can lead to poor production and quality of a product. Therefore, this study was purposefully carried out to overcome those uncertainty. This paper presents a statistical technique on the optimization of injection moulding process parameters through central composite design (CCD). In this study, an understanding of the injection moulding process and consequently its optimization is carried out by CCD based on three parameters (melt temperature, packing pressure, and cooling time) which influence the shrinkage and tensile strength of rice husk (RH) reinforced low density polyethylene (LDPE) composites. Statistical results and analysis are used to provide better interpretation of the experiment. The models are form from analysis of variance (ANOVA) method and the model passed the tests for normality and independence assumptions