Expansion of the multi-locus gene alignment approach to improve identification of the fungal species Alternaria alternata.

Alternaria alternata is part of a genus comprised of over 600 different species that occur all over the world and cause damage to humans, plants and thereby to the economy. Yet, even though some species are causing tremendous issues, the past years have shown that assigning newly found isolates to known species was rather inconsistent. Most identifications are usually done on the basis of spore morphology, chemotype and molecular markers. In this work we used strains isolated from the wild as well as commercial strains of the DSMZ (German collection of microorganisms and cell cultures) as a reference, to show, that the variation within the Alternaria alternata species is comparable to the variation between different species of the genus Alternaria in regards to spore morphology and chemotype. We compared the different methods of identification and discerned the concatenation of multiple molecular markers as the deciding factor for better identification. Up until this point, usually a concatenation of two or three traditional molecular markers was used. Some of those markers being stronger some weaker. We show that the concatenation of five molecular markers improves the likeliness of a correct assignment, thus a better distinction between the different Alternaria species.

Enhancing poultry health management through machine learning-based analysis of vocalization signals dataset.

Population expansion and rising consumer demand for nutrient-dense meals have both contributed to an increase in the consumption of animal protein worldwide. A significant portion of the meat and eggs used for human consumption come from the poultry industry. Early diagnosis and warning of infectious illnesses in poultry are crucial for enhancing animal welfare and minimizing losses in the breeding and production systems for poultry. On the other hand, insufficient techniques for early diagnosis as well as infectious disease control in poultry farms occasionally fail to stop declining productivity and even widespread death. Individual physiological, physical, and behavioral symptoms in poultry, such as fever-induced increases in body temperature, abnormal vocalization due to respiratory conditions, and abnormal behavior due to pathogenic infections, frequently represent the health status of the animal. When birds have respiratory problems, they make strange noises like coughing and snoring. The work is geared towards compiling a dataset of chickens that were both healthy and unhealthy. 100 day-old poultry birds were purchased and split into two groups at the experimental site, the poultry research farm at Bowen University. For respiratory illnesses, the first group received treatment, whereas the second group did not. After that, the birds were separated and caged in a monitored environment. To eliminate extraneous sounds and background noise that might affect the analysis, microphones were set a reasonable distance away from the birds. The data was gathered using 24-bit samples at 96 kHz. For 65 days, three times per day (morning, afternoon, and night) of audio data were continually collected. Food and water are constantly provided to the birds during this time. During this time, the birds have constant access to food and water. After 30 days, the untreated group started to sound sick with respiratory issues. This information was also noted as being unhealthy. Chickens' audio signals were recorded, saved in MA4, and afterwards converted to WAV format. This dataset's creation is intended to aid in the design of smart technologies capable of early detection and monitoring of the status of birds in poultry farms in a continuous, noninvasive, and automated way.