Development of RNAi Methods for the Mormon Cricket, Anabrus simplex (Orthoptera: Tettigoniidae).

Mormon crickets are a major rangeland pest in the western United States and are currently managed by targeted applications of non-specific chemical insecticides, which can potentially have negative effects on the environment. In this study, we took the first steps toward developing RNAi methods for Mormon crickets as a potential alternative to traditional broad-spectrum insecticides. To design an effective RNAi-based insecticide, we first generated a de novo transcriptome for the Mormon cricket and developed dsRNAs that could silence the expression of seven housekeeping genes. We then characterized the RNAi efficiencies and time-course of knockdown using these dsRNAs, and assessed their ability to induce mortality. We have demonstrated that it is possible to elicit RNAi responses in the Mormon cricket by injection, but knockdown efficiencies and the time course of RNAi response varied according to target genes and tissue types. We also show that one of the reasons for the poor knockdown efficiencies could be the presence of dsRNA-degrading enzymes in the hemolymph. RNAi silencing is possible in Mormon cricket, but more work needs to be done before it can be effectively used as a population management method.

Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Sensor.

Accurate segmentation of the iris area in input images has a significant effect on the accuracy of iris recognition and is a very important preprocessing step in the overall iris recognition process. In previous studies on iris recognition, however, the accuracy of iris segmentation was reduced when the images of captured irises were of low quality due to problems such as optical and motion blurring, thick eyelashes, and light reflected from eyeglasses. Deep learning-based iris segmentation has been proposed to improve accuracy, but its disadvantage is that it requires a long processing time. To resolve this problem, this study proposes a new method that quickly finds a rough iris box area without accurately segmenting the iris region in the input images and performs ocular recognition based on this. To address this problem of reduced accuracy, the recognition is performed using the ocular area, which is a little larger than the iris area, and a deep residual network (ResNet) is used to resolve the problem of reduced recognition rates due to misalignment between the enrolled and recognition iris images. Experiments were performed using three databases: Institute of Automation Chinese Academy of Sciences (CASIA)-Iris-Distance, CASIA-Iris-Lamp, and CASIA-Iris-Thousand. They confirmed that the method proposed in this study had a higher recognition accuracy than existing methods.

Deep RetinaNet-Based Detection and Classification of Road Markings by Visible Light Camera Sensors.

Detection and classification of road markings are a prerequisite for operating autonomous vehicles. Although most studies have focused on the detection of road lane markings, the detection and classification of other road markings, such as arrows and bike markings, have not received much attention. Therefore, we propose a detection and classification method for various types of arrow markings and bike markings on the road in various complex environments using a one-stage deep convolutional neural network (CNN), called RetinaNet. We tested the proposed method in complex road scenarios with three open datasets captured by visible light camera sensors, namely the Malaga urban dataset, the Cambridge dataset, and the Daimler dataset on both a desktop computer and an NVIDIA Jetson TX2 embedded system. Experimental results obtained using the three open databases showed that the proposed RetinaNet-based method outperformed other methods for detection and classification of road markings in terms of both accuracy and processing time.

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor.

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

SET contributes to the epithelial-mesenchymal transition of pancreatic cancer.

Pancreatic cancer has a devastating prognosis due to 80-90% of diagnostic cases occurring when metastasis has already presented. Activation of the epithelial-mesenchymal transition (EMT) is a prerequisite for metastasis because it allows for the dissemination of tumor cells to blood stream and secondary organs. Here, we sought to determine the role of SET oncoprotein, an endogenous inhibitor of PP2A, in EMT and pancreatic tumor progression. Among the two major isoforms of SET (isoform 1 and isoform 2), higher protein levels of SET isoform 2 were identified in aggressive pancreatic cancer cell lines. Overexpressing SET isoform 2, and to a lesser extent SET isoform 1, in epithelial cell lines promoted EMT-like features by inducing mesenchymal characteristics and promoting cellular proliferation, migration, invasion, and colony formation. Consistently, knockdown of SET isoforms in the mesenchymal cell line partially resisted these characteristics and promoted epithelial features. SET-induced EMT was likely facilitated by increased N-cadherin overexpression, decreased PP2A activity and/or increased expression of key EMT-driving transcription factors. Additionally, SET overexpression activated the Rac1/JNK/c-Jun signaling pathway that induced transcriptional activation of N-cadherin expression. In vivo, SET isoform 2 overexpression significantly correlated with increased N-cadherin in human PDAC and to tumor burden and metastatic ability in an orthotopic mouse tumor model. These findings identify a new role for SET in cancer and have implications for the design and targeting of SET for intervening pancreatic tumor progression.

Recognition of Damaged Arrow-Road Markings by Visible Light Camera Sensor Based on Convolutional Neural Network.

Automobile driver information as displayed on marked road signs indicates the state of the road, traffic conditions, proximity to schools, etc. These signs are important to insure the safety of the driver and pedestrians. They are also important input to the automated advanced driver assistance system (ADAS), installed in many automobiles. Over time, the arrow-road markings may be eroded or otherwise damaged by automobile contact, making it difficult for the driver to correctly identify the marking. Failure to properly identify an arrow-road marker creates a dangerous situation that may result in traffic accidents or pedestrian injury. Very little research exists that studies the problem of automated identification of damaged arrow-road marking painted on the road. In this study, we propose a method that uses a convolutional neural network (CNN) to recognize six types of arrow-road markings, possibly damaged, by visible light camera sensor. Experimental results with six databases of Road marking dataset, KITTI dataset, Málaga dataset 2009, Málaga urban dataset, Naver street view dataset, and Road/Lane detection evaluation 2013 dataset, show that our method outperforms conventional methods.

Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor.

With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods.

Using protein microarray technology to screen anti-ERCC1 monoclonal antibodies for specificity and applications in pathology.

BACKGROUND: An antibody with cross-reactivity can create unexpected side effects or false diagnostic reports if used for clinical purposes. ERCC1 is being explored as a predictive diagnostic biomarker for cisplatin-based chemotherapy. High ERCC1 expression is linked to drug resistance on cisplatin-based chemotherapy. 8F1 is one of the most commonly used monoclonal antibodies for evaluating ERCC1 expression levels in lung cancer patient tissues, but it has been noted that this antibody cross-reacts with an unknown protein. RESULTS: By using a high density protein microarray chip technology, we discovered that 8F1 not only reacts with its authentic target, ERCC1, but also cross-reacts with an unrelated nuclear membrane protein, PCYT1A. The cross-reactivity is due to a common epitope presented on these two unrelated proteins. Similar to the subcellular localization of ERCC1, IHC tests demonstrated that PCYT1A is localized mainly on nuclear membrane. In this study, we also discovered that the PCYT1A gene expression level is significantly higher than the ERCC1 gene expression level in a certain population of lung cancer patient tissue samples. To develop the best monoclonal antibody for ERCC1 IHC analysis, 18 monoclonal antibodies were generated and 6 of them were screened against our protein microarray chip. Two clones showed high mono-specificity on the protein microarray chip test and both worked for the IHC application. CONCLUSION: In summary, the 8F1 clone is not suitable for ERCC1 IHC assay due to its cross-reactivity with PCYT1A protein. Two newly generated monoclonal antibodies, 4F9 and 2E12, demonstrated ultra-specificity against ERCC1 protein and superior performance for IHC analyses.