Intact-larva Drosophila microinjection with spatial precision was achieved using a microfluidic chip. Effect of serotonin on heartrate was characterized semi-automatically.
The fruit fly or Drosophila melanogaster has been used as a promising model organism in genetics, developmental and behavioral studies as well as in the fields of neuroscience, pharmacology, and toxicology. Not only all the developmental stages of Drosophila, including embryonic, larval, and adulthood stages, have been used in experimental in vivo biology, but also the organs, tissues, and cells extracted from this model have found applications in in vitro assays. However, the manual manipulation, cellular investigation and behavioral phenotyping techniques utilized in conventional Drosophila-based in vivo and in vitro assays are mostly time-consuming, labor-intensive, and low in throughput. Moreover, stimulation of the organism with external biological, chemical, or physical signals requires precision in signal delivery, while quantification of neural and behavioral phenotypes necessitates optical and physical accessibility to Drosophila. Recently, microfluidic and lab-on-a-chip devices have emerged as powerful tools to overcome these challenges. This review paper demonstrates the role of microfluidic technology in Drosophila studies with a focus on both in vivo and in vitro investigations. The reviewed microfluidic devices are categorized based on their applications to various stages of Drosophila development. We have emphasized technologies that were utilized for tissue- and behavior-based investigations. Furthermore, the challenges and future directions in Drosophila-on-a-chip research, and its integration with other advanced technologies, will be discussed.
This paper presents a condition monitoring and combustion fault detection technique for a 12-cylinder 588 kW trainset diesel engine based on vibration signature analysis using fast Fourier transform, discrete wavelet transform, and artificial neural network. Most of the conventional fault diagnosis techniques in diesel engines are mainly based on analyzing the difference of vibration signals amplitude in the time domain or frequency spectrum. Unfortunately, for complex engines, the time- or frequency-domain approaches do not provide appropriate features solely. In the present study, vibration signals are captured from both intake manifold and cylinder heads of the engine and were analyzed in time-, frequency-, and time–frequency domains. In addition, experimental data of a 12-cylinder 588 kW diesel engine (of a trainset) are captured and the proposed method is verified via these data. Results show that power spectra of vibration signals in the low-frequency range reliably distinguish between normal and faulty conditions. However, they cannot identify the fault location. Hence, a feature extraction method based on discrete wavelet transform and energy spectrum is proposed. The extracted features from discrete wavelet transform are used as inputs in a neural network for classification purposes according to the location of sensors and faults. The experimental results verified that vibration signals acquired from intake manifold have more potential in fault detection. In addition, the capacity of discrete wavelet transform and artificial neural network in detection and diagnosis of faulty cylinders subjected to the abnormal fuel injection was revealed in a complex diesel engine. Beside condition monitoring of the engine, a two-step fault detection method is proposed, which is more reliable than other one-step methods for complex engines. The average condition monitoring performance is from 93.89% up to 99.17%, based on fault location and sensor placement, and the minimum classification performance is 98.34%.
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