No abstract

In nature, protozoa play a major role in controlling bacterial populations. This paper proposes a microfluidic device for the study of protozoa behaviors change due to their chemotactic response in the presence of bacterial cells. A three-channel microfluidic device was designed using a nitrocellulose membrane into which channels were cut using a laser cutter. The membrane was sandwiched between two glass slides; a Euglena suspension was then allowed to flow through the central channel. The two side channels were filled with either, 0.1% peptone as a negative control, or a Listeria suspension respectively. The membrane design prevented direct interaction but allowed Euglena cells to detect Listeria cells as secretions diffused through the nitrocellulose membrane. A significant number of Euglena cells migrated toward the chambers near the bacterial cells, indicating a positive chemotactic response of Euglena toward chemical cues released from Listeria cells. Filtrates collected from Listeria suspension with a series of molecular weight cutoffs (3k, 10k and 100k) were examined in Euglena chemotaxis tests. Euglena cells were attracted to all filtrates collected from the membrane filtration with different molecular weight cutoffs, suggesting small molecules from Listeria might be the chemical cues to attract protozoa. Headspace volatile organic compounds (VOC) released from Listeria were collected, spiked to 0.1% peptone and tested as the chemotactic effectors. It was discovered that the Euglena cells responded quickly to Listeria VOCs including decanal, 3,5- dimethylbenzaldehyde, ethyl acetate, indicating bacterial VOCs were used by Euglena to track the location of bacteria.

No abstract

We developed an audio signal processing algorithm that detects rales (gurgling noises that are a distinct symptom of common respiratory diseases in poultry). We derived features from the audio by calculating mel frequency cepstral coefficients (MFCCs), clustering the MFCC vectors, and examining the distribution of cluster indices over a window of time. The features are classified with a C4.5 decision tree. Our training data consisted of eight minutes of manually labeled audio selected from 25 days of continuous recording from a controlled study. The experiment group was challenged with the infectious bronchitis virus and became sick, while the control group remained healthy. We tested the algorithm on the entire dataset and obtained results that match the course of the disease. Algorithms such as this could be used to continuously monitor chickens in commercial poultry farms, providing an early warning system that could significantly reduce the costs incurred from disease.

No abstract