CNN-RNN based method for license plate recognition

Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle (e.g., cars) have numbers with dark background, while public vehicle (taxis/cabs) have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks namely, BLSTM (Bi-Directional Long Short Term Memory), for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting (DCV), which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification

Network pharmacology combined with Mendelian randomization analysis to identify the key targets of renin-angiotensin-aldosterone system inhibitors in the treatment of diabetic nephropathy

BackgroundDiabetic Nephropathy (DN) is one of the microvascular complications of diabetes. The potential targets of renin-angiotensin-aldosterone system (RAAS) inhibitors for the treatment of DN need to be explored.MethodsThe GSE96804 and GSE1009 datasets, 729 RAAS inhibitors-related targets and 6,039 DN-related genes were derived from the public database and overlapped with the differentially expressed genes (DN vs. normal) in GSE96804 to obtain the candidate targets. Next, key targets were screened via the Mendelian randomization analysis and expression analysis. The diagnostic nomogram was constructed and assessed in GSE96804. Additionally, enrichment analysis was conducted and a ‘core active ingredient-key target-disease pathway’ network was established. Finally, molecular docking was performed.ResultsIn total, 60 candidate targets were derived, in which CTSC and PDE5A were screened as the key targets and had a causal association with DN as the protective factors (P < 0.05, OR < 1). Further, a nomogram exhibited pretty prediction efficiency. It is indicated that Benadryl hydrochloride might play a role in the DN by affecting the pathways of ‘cytokine cytokine receptor interaction’, etc. targeting the CTSC. Moreover, PDE5A might be involved in ‘ECM receptor interaction’, etc. for the effect of NSAID, captopril, chlordiazepoxide on DN. Molecular docking analysis showed a good binding ability of benadryl hydrochloride and CTSC, NSAID and PDE5A. PTGS2, ITGA4, and ANPEP are causally associated with acute kidney injury.ConclusionCTSC and PDE5A were identified as key targets for RAAS inhibitors in the treatment of DN, which might provide some clinical significance in helping to diagnose and treat DN. Among the targets of RAAS inhibitors, PTGS2, ITGA4 and ANPEP have a causal relationship with acute kidney injury, which is worthy of further clinical research

Nrf2 Deficiency Exaggerates Doxorubicin-Induced Cardiotoxicity and Cardiac Dysfunction

The anticancer therapy of doxorubicin (Dox) has been limited by its acute and chronic cardiotoxicity. In addition to a causative role of oxidative stress, autophagy appears to play an important role in the regulation of Dox-induced cardiotoxicity. However, the underlying mechanisms remain unclear. Accordingly, we explored a role of nuclear factor erythroid-2 related factor 2 (Nrf2) in Dox-induced cardiomyopathy with a focus on myocardial oxidative stress and autophagic activity. In wild type (WT) mice, a single intraperitoneal injection of 25 mg/kg Dox rapidly induced cardiomyocyte necrosis and cardiac dysfunction, which were associated with oxidative stress, impaired autophagy, and accumulated polyubiquitinated protein aggregates. However, these Dox-induced adverse effects were exaggerated in Nrf2 knockout (Nrf2−/−) mice. In cultured cardiomyocytes, overexpression of Nrf2 increased the steady levels of LC3-II, ameliorated Dox-induced impairment of autophagic flux and accumulation of ubiquitinated protein aggregates, and suppressed Dox-induced cytotoxicity, whereas knockdown of Nrf2 exerted opposite effects. Moreover, the exaggerated adverse effects in Dox-intoxicated Nrf2 depleted cardiomyocytes were dramatically attenuated by forced activation of autophagy via overexpression of autophagy related gene 5 (Atg5). Thus, these results suggest that Nrf2 is likely an endogenous suppressor of Dox-induced cardiotoxicity by controlling both oxidative stress and autophagy in the heart

How does users' interest influence their click behavior?: evidence from Chinese online video media

Interest is one of the main factors motivating an individual's behavior, and its effect in the learning process has been widely confirmed in educational psychology. The purpose of this study was to explore the influence of individual interest, topic interest and situational interest on the user's video click behavior in the online video browsing situation. We constructed an online experiment in which each participant was asked to use questionnaires to assess their responses to video categories, titles, and covers from the video-sharing website, Bilibili. Based on these responses, we obtained individual interests, topic interests, situational interests, and click behavior of the participants toward the videos. Correlation, regression and mediation analyses were conducted to explore the effects and mechanisms of the three interests on click behavior. The results found: (1) individual interest may have a positive but relatively weaker effect on click behavior, and (2) topic interest and situational interest positively predicted click behavior in all categories. The mediation analysis found: (1) in the otomads and fashion categories, the effect of individual interest on click behavior was partially mediated by topic and situational interest, and (2) in the anime, digits, life, dance, music, game, entertainment, and knowledge categories, the effect of individual interest on click behavior was fully mediated by topic interest and situational interest. These results revealed the facilitating effects and different effect modes of individual, topic, and situational interest on click behavior. These findings shed light on the influence mechanism of interests on video click behavior in different video categories and provide new insights into related applications such as recommender