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Safety management of the process industry plays a significant role in protecting life and property. Fault diagnosis techniques have been widely utilized for safety management of the process industr...

Background: Sophocarpine (SC) is a major alkaloid extracted from Sophora alopecuroides L. Our previous study showed that SC has analgesic effects on neuropathic pain induced by chronic constriction injury (CCI). However, the exact analgesic mechanism of SC on neuropathic pain has not yet been elucidated. Objectives: The current study aimed to examine the anti-neuropathic pain effects of SC on the HMGB1/TLR4/NF-κB signaling pathway to explore its analgesic mechanism in neuropathic pain. Methods: In this experimental study, the neuropathic pain mouse model was established by CCI of the sciatic nerve in a university-affiliated animal lab, China, 2016. Mechanical withdrawal threshold (MWT), thermal withdrawal latency (TWL), cold withdrawal threshold (CWT), and tail-curling latency (TCL) were used to assess the antinociceptive effect of SC in neuropathic pain mice. The mRNA and protein expression levels of HMGB1, TLR4, NF-κB, p-NF-κB, TNF-α, and IL-6 in the spinal cord of neuropathic pain mice were detected by reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting. Results: Treatment with 40 and 20 mg/kg of SC was effective in increasing MWT, lengthening TWL, reducing CWT, and prolonging TCL in mice with neuropathic pain induced by CCI. Compared to the neuropathic-pain model group, treatment with 40 mg/kg of SC could effectively down-regulate HMGB1, TLR4, NF-κB, p-NF-κB, TNF-α, and IL-6 mRNA and protein expression levels in the spinal cord of mice with neuropathic pain induced by CCI. Conclusions: Our results showed that SC has analgesic effects on neuropathic pain induced by CCI, and its analgesic mechanism may be related to down-regulating the HMGB1/TLR4/NF-κB signaling pathway.

To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts. For many tasks—including but not limited to systematic reviews and meta-analyses—the scientific literature needs to be checked systematically. Scholars and practitioners currently screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that active learning can yield far more efficient reviewing than manual reviewing while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice. It is a challenging task for any research field to screen the literature and determine what needs to be included in a systematic review in a transparent way. A new open source machine learning framework called ASReview, which employs active learning and offers a range of machine learning models, can check the literature efficiently and systemically.

Background To investigate the mechanisms of lung adenocarcinoma cell metastasis and provide a theoretical basis for the in-depth study of lung adenocarcinoma. Methods A549 cells are incubated with different concentrations of Furin inhibitor for indicated times. The proliferation and migration were confirmed with MTT, colony formation, wound Healing and Transwell assayes. Hochest 33342 / PI double staining was used to detect apoptosis. Cell migration and apoptosis associated proteins were analysed by enzyme-linked immunosorbent assay (ELISA) and western blot. Results We have found that Furin inhibitor play a significant role in inhibition A549 cell growth. And we also found cell migration was inhibited significantly upon Furin inhibitor treatment. Conclusion The proliferration and migration of A549 cell were inhibited by Furin inbitor through down-regulation the expression of migration and apoptosis related proteins.

In machine vision and the vehicle recognition system? removal of moving vehicle shadows is a significant topic. In this paper, we propose a novel method to detect shadows in traffic video sequences. Firstly, a set of moving regions are segmented from the video sequence using a background subtraction technique. Secondly, the fast normalized cross-correlation (FNCC) is adopted to detect shadows in moving regions from grayscale video sequences. By utilizing three sum-table schemes, the FNCC algorithm dramatically reduces the computational complexity compared to the traditional normalized cross correlation (NCC) algorithm. And our experimental results demonstrate that the proposed shadows removal method is accurate and efficient.