MicroRNA-140-5p inhibits cellular proliferation, migration and invasion by downregulating AKT/STAT3/NF-κB pathway in breast carcinoma cells

MicroRNA-140-5p (miR-140-5p) plays a pivotal role in human cancers. However, its role and molecular mechanisms in breast carcinoma are not fully explored. Using miR-140-5p transfected breast cancer cell line MDA-MB-231, several in vitro experiments were performed and described in this paper. They consist of the cell proliferation assay, wound healing assay, transwell assay, colony formation assays and qRTPCR. Expression levels of target proteins were determined using western blotting. In addition, experiments on animal models were performed to study the possible role of miR-140-5p in tumorigenesis of breast carcinoma cells. The induction of experimental breast tumor in mice model was achieved through the incorporation of MDA-MB-231 tumor cells subcutaneously into the middle left side of the mice. The results showed that miR-140-5p up-regulation significantly suppresses proliferation, cellular invasion and migration of breast carcinoma cells. Furthermore, miR-140-5p up-regulation stops breast cancer cells at G0/G1 phase. The results of the animal model indicated that up-regulation of miR-140-5p suppresses its tumorigenic ability. Moreover, we also found that miR-140-5p up-regulation reduces the phosphorylation level of STAT3, p65, and AKT. In addition, miR-140-5p overexpression significantly decreases CDK2 expression while increasing E-cadherin expression level. These data revealed that miR-140-5p suppressed tumor progression of breast carcinoma cells through inhibition of the AKT/STAT3/NF-κB pathway. Taken the present study results together, we can conclude that miR-140-5p may act as a novel target in microRNA-targeting anticancer strategy for the treatment of breast cancer

Prior knowledge-based deep learning method for indoor object recognition and application

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision

HuatuoGPT, towards Taming Language Model to Be a Doctor

In this paper, we present HuatuoGPT, a large language model (LLM) for medical consultation. The core recipe of HuatuoGPT is to leverage both \textit{distilled data from ChatGPT} and \textit{real-world data from doctors} in the supervised fine-tuned stage. The responses of ChatGPT are usually detailed, well-presented and informative while it cannot perform like a doctor in many aspects, e.g. for integrative diagnosis. We argue that real-world data from doctors would be complementary to distilled data in the sense the former could tame a distilled language model to perform like doctors. To better leverage the strengths of both data, we train a reward model to align the language model with the merits that both data bring, following an RLAIF (reinforced learning from AI feedback) fashion. To evaluate and benchmark the models, we propose a comprehensive evaluation scheme (including automatic and manual metrics). Experimental results demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets. It is worth noting that by using additional real-world data and RLAIF, the distilled language model (i.e., HuatuoGPT) outperforms its teacher model ChatGPT in most cases. Our code, data, and models are publicly available at \url{https://github.com/FreedomIntelligence/HuatuoGPT}. The online demo is available at \url{https://www.HuatuoGPT.cn/}

The NLRP3 inflammasome is involved in resident intruder paradigm-induced aggressive behaviors in mice

Background: Aggressive behaviors are one of the most important negative behaviors that seriously endangers human health. Also, the central para-inflammation of microglia triggered by stress can affect neurological function, plasticity, and behavior. NLRP3 integrates stress-related signals and is a key driver of this neural para-inflammation. However, it is unclear whether the NLRP3 inflammasome is implicated in the development of aggressive behaviors.Methods: First, aggressive behavior model mice were established using the resident intruder paradigm. Then, aggressive behaviors were determined with open-field tests (OFT), elevated plus-maze (EPM), and aggressive behavior tests (AT). Moreover, the expression of P2X7R and NLRP3 inflammasome complexes were assessed by immunofluorescence and Western blot. The levels of NLRP3 and inflammatory cytokines were evaluated using enzyme-linked immunosorbent assay (ELISA) kits. Finally, nerve plasticity damage was observed by immunofluorescence, transmission electron microscope, and BrdU staining.Results: Overall, the resident intruder paradigm induced aggressive behaviors, activated the hippocampal P2X7R and NLRP3 inflammasome, and promoted the release of proinflammatory cytokines IL-1β in mice. Moreover, NLRP3 knockdown, administration of P2X7R antagonist (A804598), and IL-1β blocker (IL-1Ra) prevented NLRP3 inflammasome-driven inflammatory responses and ameliorated resident intruder paradigm-induced aggressive behaviors. Also, the resident intruder paradigm promoted the activation of mouse microglia, damaging synapses in the hippocampus, and suppressing hippocampal regeneration in mice. Besides, NLRP3 knockdown, administration of A804598, and IL-1Ra inhibited the activation of microglia, improved synaptic damage, and restored hippocampal regeneration.Conclusion: The NLRP3 inflammasome-driven inflammatory response contributed to resident intruder paradigm-induced aggressive behavior, which might be related to neuroplasticity. Therefore, the NLRP3 inflammasome can be a potential target to treat aggressive behavior-related mental illnesses

Urban Hotspot Area Detection Using Nearest-Neighborhood-Related Quality Clustering on Taxi Trajectory Data

Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It is of great significance to mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics and the operational status of urban traffic. The existing clustering methods mainly concentrate on the number of objects contained in an area within a specified size, neglecting the impact of the local density and the tightness between objects. Hence, a novel algorithm is proposed for detecting urban hotspots from taxi trajectory data based on nearest neighborhood-related quality clustering techniques. The proposed spatial clustering algorithm not only considers the maximum clustering in a limited range but also considers the relationship between each cluster center and its nearest neighborhood, effectively addressing the clustering issue of unevenly distributed datasets. As a result, the proposed algorithm obtains high-quality clustering results. The visual representation and simulated experimental results on a real-life cab trajectory dataset show that the proposed algorithm is suitable for inferring urban hotspot areas, and that it obtains better accuracy than traditional density-based methods

Research on Sentiment Classification of Online Travel Review Text

In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research

MicroRNA-140-5p inhibits cellular proliferation, migration and invasion by downregulating AKT/STAT3/NF-κB pathway in breast carcinoma cells

MicroRNA-140-5p (miR-140-5p) plays a pivotal role in human cancers. However, its role and molecular mechanisms in breast carcinoma are not fully explored. Using miR-140-5p transfected breast cancer cell line MDA-MB-231, several in vitro experiments were performed and described in this paper. They consist of the cell proliferation assay, wound healing assay, transwell assay, colony formation assays and qRTPCR. Expression levels of target proteins were determined using Western blotting. In addition, experiments on animal models were performed to study the possible role of miR-140-5p in tumorigenesis of breast carcinoma cells. The induction of experimental breast tumor in mice model was achieved through the incorporation of MDA-MB-231 tumor cells subcutaneously into the middle left side of the mice. The results showed that miR-140-5p up-regulation significantly suppresses proliferation, cellular invasion and migration of breast carcinoma cells. Furthermore, miR-140-5p up-regulation stops breast cancer cells at G0/G1 phase. The results of the animal model indicated that up-regulation of miR-140-5p suppresses its tumorigenic ability. Moreover, we also found that miR-140-5p up-regulation reduces the phosphorylation level of STAT3, p65, and AKT. In addition, miR-140-5p overexpression significantly decreases CDK2 expression while increasing E-cadherin expression level. These data revealed that miR-140-5p suppressed tumor progression of breast carcinoma cells through inhibition of the AKT/STAT3/NF-κB pathway. Taken the present study results together, we can conclude that miR-140-5p may act as a novel target in microRNA-targeting anticancer strategy for the treatment of breast cancer

A Framework of Abnormal Behavior Detection and Classification Based on Big Trajectory Data for Mobile Networks

Big trajectory data feature analysis for mobile networks is a popular big data analysis task. Due to the large coverage and complexity of the mobile networks, it is difficult to define and detect anomalies in urban motion behavior. Some existing methods are not suitable for the detection of abnormal urban vehicle trajectories because they use the limited single detection techniques, such as determining the common patterns. In this study, we propose a framework for urban trajectory modeling and anomaly detection. Our framework takes into account the fact that anomalous behavior manifests the overall shape of unusual locations and trajectories in the spatial domain as well as the way these locations appear. Therefore, this study determines the peripheral features required for anomaly detection, including spatial location, sequence, and behavioral features. Then, we explore sports behaviors from the three types of features and build a taxi trajectory model for anomaly detection. Anomaly detection, including sports behaviors, are (i) detour behavior detection using an algorithm for global router anomaly detection of trajectories having a pair of same starting and ending points; this method is based on the isolation forest algorithm; (ii) local speed anomaly detection based on the DBSCAN algorithm; and (iii) local shape anomaly detection based on the local outlier factor algorithm. Using a real-life dataset, we demonstrate the effectiveness of our methods in detecting outliers. Furthermore, experiments show that the proposed algorithms perform better than the classical algorithm in terms of high accuracy and recall rate; thus, the proposed methods can accurately detect drivers’ abnormal behavior