A Proposed Taxonomy of Anaerobic Fungi (Class Neocallimastigomycetes) Suitable for Large-Scale Sequence-Based Community Structure Analysis

Anaerobic fungi are key players in the breakdown of fibrous plant material in the rumen, but not much is known about the composition and stability of fungal communities in ruminants. We analyzed anaerobic fungi in 53 rumen samples from farmed sheep (4 different flocks), cattle, and deer feeding on a variety of diets. Denaturing gradient gel electrophoresis fingerprinting of the internal transcribed spacer 1 (ITS1) region of the rrn operon revealed a high diversity of anaerobic fungal phylotypes across all samples. Clone libraries of the ITS1 region were constructed from DNA from 11 rumen samples that had distinctly different fungal communities. A total of 417 new sequences were generated to expand the number and diversity of ITS1 sequences available. Major phylogenetic groups of anaerobic fungi in New Zealand ruminants belonged to the genera Piromyces, Neocallimastix, Caecomyces and Orpinomyces. In addition, sequences forming four novel clades were obtained, which may represent so far undetected genera or species of anaerobic fungi. We propose a revised phylogeny and pragmatic taxonomy for anaerobic fungi, which was tested and proved suitable for analysis of datasets stemming from high-throughput next-generation sequencing methods. Comparing our revised taxonomy to the taxonomic assignment of sequences deposited in the GenBank database, we believe that >29% of ITS1 sequences derived from anaerobic fungal isolates or clones are misnamed at the genus level

Polysaccharidase and glycosidase production of avicel grown rumen fungus Orpinomyces sp. GMLF5

Extracellular and cell-associated enzyme preparations were obtained from ruminal anaerobic fungi Orpinomyces sp. GMLF5 grown in culture containing microcrystalline cellulose (avicel) as sole energy source and degradation capacities of the preparations towards several polysaccharides and glycosides were studied. Fungus showed substantial increases in xylanase, carboxymethyl cellulase (CMCase), lichenase, amylase, β-xylosidase, β-glucosidase and α-L-arabinofuranosidase activities between 72 and 168 hours. High amounts of cell associated β-xylosidase were noted in 4 and 5 days old cultures. Optimum temperature and pH of the polysaccharidases were found at 50 °C and 6.0–6.5, respectively. Xylanase was found to be virtually stable at 50°C, CMCase and lichenase were stable at 40 °C for 200 min, however amylase was found more sensitive to heat treatment. The fibrolytic enzymes of the isolate GMLF5 were observed to be capable of hydrolyze the avicel

Effect of polyethylene glycol on in vitro gas production, metobolizable energy and organic matter digestibility of Quercus cerris leaves

In this study the effects of Polyethylene glycol (PEG) on in vitro gas production, organic matter digestibility (OMD) and metobolizable energy (ME) contents of Quercus cerris leaves were investigated. Gas production was measured at 3, 6, 12, 24, 48, 72 and 96 hours in the presence (15, 30, 60 and 90 mg) and in the absence of PEG (MW 8000), and gas production kinetics were estimated using the equation y = a + b (1 - e-ct). PEG had a significant effect on in vitro gas production, OMD and ME. The OMD and ME contents of Quercus cerris leaves increased with increased level of PEG. The mean increase in OMD per mg PEG supplementation was 0.121 digestibility units although the mean increase in ME per mg PEG supplementation was 0.0185 metabolizable energy units. In vitro gas production showed positive responses to incubation of Quercus cerris leaf samples with tannin binding agent (PEG) in comparison to non-treated samples. The improvement in gas production, OMD and ME depended on the level of PEG supplementation. The improvement in gas production, OMD and ME with PEG emphasizes the negative effect of tannins on digestibility. However PEG supplementation to improve the nutritive value of tannin containing tree leaves should be further analysed in detail whether or not it is economical before large scale implementation

beta-Glucanase-producing bacterial culture improves performance and nutrient utilization and alters gut morphology of broilers fed a barley-based diet

The present study was conducted to evaluate the efficacy of an Escherichia coli DH5-alpha strain producing beta-glucanase from Streptococcus bovis on growth performance, diet utilization and the gut morphology of broilers. One hundred and twenty 1-day-old chicks (Cobb 500) were randomly divided into three groups with five replicate pens of eight chicks and assigned to receive maize-based diet (MBD)(+), barley-based diet (BBD), BBD with addition of bacteria producing P-glucanas

AI-Generated Messaging for Life Events Using Structured Prompts: A Comparative Study of GPT With Human Experts and Machine Learning

Large Language Models (LLMs) are increasingly used to generate social media posts, emails, narratives, and other forms of communication, yet few studies systematically assess how well these outputs align with their original prompt intent. This study evaluates the effectiveness of a zero-shot structured narrative prompt for generating 24 000 life event messages (birth, death, hiring, and firing events) using OpenAI’s GPT-4. A manually tagged sample of 2880 messages shows that 87.43% align with the intended life event when framed as X (formerly Twitter) posts. To scale this evaluation, we train nine machine learning (ML) models, including BERT, Keras-based architectures, eXtreme Gradient Boosting, Random Forest, and Support Vector Machines, per life event type, resulting in 36 binary classifiers. We apply an ensemble approach based on simple majority agreement to classify the remaining 21 120 messages. Manual validation of a 1% sample (n = 212) confirms a 90.57% match rate with human reviewers, with binomial tests confirming statistical significance above a 75% threshold across all event types (P-values ranging from 0.00025 to 0.0115). While valid messages are reliably identified, classifying invalid cases remains more challenging. This work offers a reproducible framework for validating AI-generated messaging and provides practical guidance for prompt-based applications. Limitations include the narrow event scope, exclusive use of English text, and reliance on structured prompts, which may not generalize to open-ended tasks