Gene silencing in tick cell lines using small interfering or long double-stranded RNA

Gene silencing by RNA interference (RNAi) is an important research tool in many areas of biology. To effectively harness the power of this technique in order to explore tick functional genomics and tick-microorganism interactions, optimised parameters for RNAi-mediated gene silencing in tick cells need to be established. Ten cell lines from four economically important ixodid tick genera (Amblyomma, Hyalomma, Ixodes and Rhipicephalus including the sub-species Boophilus) were used to examine key parameters including small interfering RNA (siRNA), double stranded RNA (dsRNA), transfection reagent and incubation time for silencing virus reporter and endogenous tick genes. Transfection reagents were essential for the uptake of siRNA whereas long dsRNA alone was taken up by most tick cell lines. Significant virus reporter protein knockdown was achieved using either siRNA or dsRNA in all the cell lines tested. Optimum conditions varied according to the cell line. Consistency between replicates and duration of incubation with dsRNA were addressed for two Ixodes scapularis cell lines; IDE8 supported more consistent and effective silencing of the endogenous gene subolesin than ISE6, and highly significant knockdown of the endogenous gene 2I1F6 in IDE8 cells was achieved within 48 h incubation with dsRNA. In summary, this study shows that gene silencing by RNAi in tick cell lines is generally more efficient with dsRNA than with siRNA but results vary between cell lines and optimal parameters need to be determined for each experimental system

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions

Utilizing Image Processing and the YOLOv3 Network for Real-Time Traffic Light Control

In this study, different strategies used to count vehicles and people in different image areas at a street intersection were analyzed to obtain counts at appropriate times suitable for real-time control of a traffic light. To achieve this, video recordings of cameras placed at the intersection were used to test and verify image processing algorithms and deep learning using the YOLOv3 network implemented on a 4 GB RAM Jetson Nano card. We counted the vehicles and people that stopped and crossed the polygons to delimit the different areas of interest, with a maximum error of ±2 in the validation tests for all cases. In addition, as a strategy, we combined the images from both cameras into a single one, thereby allowing us to make a single detection and subsequently determine if they are inside or outside the polygons used in separating the areas of interest with the respective counts. Furthermore, this enabled us to obtain information on vehicles and people stopped and crossing in a time of 0.73 s on average. Hence, it was established that the inclusion of the control algorithm is appropriate for real-time control of traffic lights

A chromosome-level genome of Astyanax mexicanus surface fish for comparing population-specific genetic differences contributing to trait evolution

Identifying the genetic factors that underlie complex traits is central to understanding the mechanistic underpinnings of evolution. Cave-dwelling Astyanax mexicanus populations are well adapted to subterranean life and many populations appear to have evolved troglomorphic traits independently, while the surface-dwelling populations can be used as a proxy for the ancestral form. Here we present a high-resolution, chromosome-level surface fish genome, enabling the first genome-wide comparison between surface fish and cavefish populations. Using this resource, we performed quantitative trait locus (QTL) mapping analyses and found new candidate genes for eye loss such as dusp26. We used CRISPR gene editing in A. mexicanus to confirm the essential role of a gene within an eye size QTL, rx3, in eye formation. We also generated the first genome-wide evaluation of deletion variability across cavefish populations to gain insight into this potential source of cave adaptation. The surface fish genome reference now provides a more complete resource for comparative, functional and genetic studies of drastic trait differences within a species