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Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper-based on imbalanced data-proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation.Autism spectrum disorder (ASD) is a neurodevelopmental disorder whose pathogenesis is unclear and is affected by both genetic and environmental factors. The microRNAs (miRNAs) are a kind of single-stranded non-coding RNA with 20-22 nucleotides, which normally inhibit their target mRNAs at a post-transcriptional level. miRNAs are involved in almost all biological processes and are closely related to ASD and many other diseases. In this review, we summarize relevant miRNAs in ASD, and analyze dysregulated miRNAs in brain tissues and body fluids of ASD patients, which may contribute to the pathogenesis and diagnosis of ASD.Being biodegradable and biocompatible are crucial characteristics for biomaterial used for medical and biomedical applications. Vegetable oil-based polyols are known to contribute both the biodegradability and biocompatibility of polyurethanes; however, petrochemical-based polyols were often incorporated to improve the thermal and mechanical properties of polyurethane. In this work, palm oil-based polyester polyol (PPP) derived from epoxidized palm olein and glutaric acid was reacted with isophorone diisocyanate to produce an aliphatic polyurethane, without the incorporation of any commercial petrochemical-based polyol. The effects of water content and isocyanate index were investigated. The polyurethanes produced consisted of > 90% porosity with interconnected micropores and macropores (37-1700 µm) and PU 1.0 possessed tensile strength and compression stress of 111 kPa and 64 kPa. The polyurethanes with comparable thermal stability, yet susceptible to enzymatic degradation with 7-59% of mass loss after 4 weeks of treatment. The polyurethanes demonstrated superior water uptake (up to 450%) and did not induce significant changes in pH of the medium. The chemical changes of the polyurethanes after enzymatic degradation were evaluated by FTIR and TGA analyses. The polyurethanes showed cell viability of 53.43% and 80.37% after 1 and 10 day(s) of cytotoxicity test; and cell adhesion and proliferation in cell adhesion test. The polyurethanes produced demonstrated its potential as biomaterial for soft tissue engineering applications.Pycnogenol® (an extract of the bark of French maritime pine tree) is used for dietary supplement and known to have excellent antioxidative efficacy. However, there are few reports on neuroprotective effect of Pycnogenol® supplementation and its mechanisms against ischemic injury following transient forebrain ischemia (TFI) in gerbils. Now, we examined neuroprotective effect and its mechanisms of Pycnogenol® in the gerbils with 5-min TFI, which evokes a significant death (loss) of pyramidal cells located in the cornu ammonis (CA1) region of gerbil hippocampus from 4-5 days post-TFI. Gerbils were pretreated with 30, 40, and 50 mg/kg of Pycnogenol® once a day for 7 days before TFI surgery. Treatment with 50 mg/kg, not 30 or 40 mg/kg, of Pycnogenol® potently protected learning and memory, as well as CA1 pyramidal cells, from ischemic injury. Treatment with 50 mg/kg Pycnogenol® significantly enhanced immunoreactivity of antioxidant enzymes (superoxide dismutases and catalase) in the pyramidal cells before and after TFI induction. Furthermore, the treatment significantly reduced the generation of superoxide anion, ribonucleic acid oxidation and lipid peroxidation in the pyramidal cells. Moreover, interestingly, its neuroprotective effect was abolished by administration of sodium azide (a potent inhibitor of SODs and catalase activities). Taken together, current results clearly indicate that Pycnogenol® supplementation can prevent neurons from ischemic stroke through its potent antioxidative role.Aging poses a big challenge in all aspects of social governance in China. BzATP triethylammonium A coherent and focused aging policy response that spans multiple sectors of government has been undertaken to achieve the goal of "Healthy Aging". From an historical perspective, this paper uses a bibliometric analysis method to probe into the evolution of Chinese aging policies from 1978 to 2019, and the roles of core government agencies in policy-making. We obtained 226 Chinese aging policies from the PKULaw Database and the websites of the government departments. Co-word analyses and network analyses were applied in mapping the topics of aging policies and collaboration among the agencies. Gephi software was used to visualize the most frequently used keywords and their network graphs. Findings are as follows. Firstly, the development of the aging policy system in China has undergone two phases, from focusing on basic security to emphasizing the rights and health of the elderly. Secondly, the network structure of aging policy-making departments presents a distinct edge-core layer. More and more government agencies have become involved in the formulation of aging policies. But collaboration among the agencies is insufficient. Thirdly, pilot promotion is the main tool for implementing aging policies.In the present study, we evaluated four small molecule affinity-based probes based on agarose-immobilized benzamidine (ABA), O-Phospho-L-Tyrosine (pTYR), 8-Amino-hexyl-cAMP (cAMP), or 8-Amino-hexyl-ATP (ATP) for their ability to remove high-abundant proteins such as serum albumin from plasma samples thereby enabling the detection of medium-to-low abundant proteins in plasma samples by mass spectrometry-based proteomics. We compared their performance with the most commonly used immunodepletion method, the Multi Affinity Removal System Human 14 (MARS14) targeting the top 14 most abundant plasma proteins and also the ProteoMiner protein equalization method by label-free quantitative liquid chromatography tandem mass spectrometry (LC-MSMS) analysis. The affinity-based probes demonstrated a high reproducibility for low-abundant plasma proteins, down to picomol per mL levels, compared to the Multi Affinity Removal System (MARS) 14 and the Proteominer methods, and also demonstrated superior removal of the majority of the high-abundant plasma proteins. The ABA-based affinity probe and the Proteominer protein equalization method performed better compared to all other methods in terms of the number of analyzed proteins. All the tested methods were highly reproducible for both high-abundant plasma proteins and low-abundant proteins as measured by correlation analyses of six replicate experiments. In conclusion, our results demonstrated that small-molecule based affinity-based probes are excellent alternatives to the commonly used immune-depletion methods for proteomic biomarker discovery studies in plasma. Data are available via ProteomeXchange with identifier PXD020727.Pathogenic germline variants in Breast Cancer 1/2 (BRCA) genes confer increased cancer risk. Understanding BRCA status/risk can enable family cascade screening and improve cancer outcomes. However, more than half of the families do not communicate family cancer history/BRCA status, and cancer outcomes differ according to parent of origin (i.e., maternally vs. paternally inherited pathogenic variant). We aimed to explore communication patterns around family cancer history/BRCA risk according to parent of origin. We analyzed qualitative interviews (n = 97) using template analysis and employed the Theory of Planned Behavior (TPB) to identify interventions to improve communication. Interviews revealed sub-codes of 'male stoicism and 'paternal guilt' that impede family communication (template code gender scripting). Conversely, 'fatherly protection' and 'female camaraderie' promote communication of risk. The template code 'dysfunctional family communication' was contextualized by several sub-codes ('harmful negligence', 'intra-family ignorance' and 'active withdrawal of support') emerging from interview data. Sub-codes 'medical misconceptions' and 'medical minimizing' deepened our understanding of the template code 'medical biases'. Importantly, sub-codes of 'informed physicians' and 'trust in healthcare' mitigated bias. Mapping findings to the TPB identified variables to tailor interventions aimed at enhancing family communication of risk and promoting cascade screening. In conclusion, these data provide empirical evidence of the human factors impeding communication of family BRCA risk. Tailored, theory-informed interventions merit consideration for overcoming blocked communication and improving cascade screening uptake.Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user's device, which can cause the leakage of user's privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.Kubernetes, an open-source container orchestration platform, enables high availability and scalability through diverse autoscaling mechanisms such as Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler and Cluster Autoscaler. Amongst them, HPA helps provide seamless service by dynamically scaling up and down the number of resource units, called pods, without having to restart the whole system. Kubernetes monitors default Resource Metrics including CPU and memory usage of host machines and their pods. On the other hand, Custom Metrics, provided by external software such as Prometheus, are customizable to monitor a wide collection of metrics. In this paper, we investigate HPA through diverse experiments to provide critical knowledge on its operational behaviors. We also discuss the essential difference between Kubernetes Resource Metrics (KRM) and Prometheus Custom Metrics (PCM) and how they affect HPA's performance. Lastly, we provide deeper insights and lessons on how to optimize the performance of HPA for researchers, developers, and system administrators working with Kubernetes in the future.