Parkinson’s disease (PD) is a chronic and progressive neurodegenerative disease caused by degeneration of dopaminergic neurons in the substantia nigra. Existing pharmaceutical treatments offer alleviation of symptoms but cannot delay disease progression and are often associated with significant side effects. Clinical studies have demonstrated that acupuncture may be beneficial for PD treatment, particularly in terms of ameliorating PD symptoms when combined with anti-PD medication, reducing the required dose of medication and associated side effects. During early stages of PD, acupuncture may even be used to replace medication. It has also been found that acupuncture can protect dopaminergic neurons from degeneration via antioxidative stress, anti-inflammatory, and antiapoptotic pathways as well as modulating the neurotransmitter balance in the basal ganglia circuit. Here, we review current studies and reflect on the potential of acupuncture as a novel and effective treatment strategy for PD. We found that particularly during the early stages, acupuncture may reduce neurodegeneration of dopaminergic neurons and regulate the balance of the dopaminergic circuit, thus delaying the progression of the disease. The benefits of acupuncture will need to be further verified through basic and clinical studies.

Background . Nowadays, acute intracerebral hemorrhage stroke (AICH) still causes higher mortality. Liangxue Tongyu Formula (LXTYF), originating from a traditional Chinese medicine (TCM) prescription, is widely used as auxiliary treatment for AICH. Objective . To dig into the multicomponent, multitarget, and multipathway mechanism of LXTYF on treating AICH via network pharmacology and RNA-seq. Methods . Network pharmacology analysis was used by ingredient collection, target exploration and prediction, network construction, and Gene Ontology (GO) and KEGG analysis, with the Cytoscape software and ClusterProfiler package in R. The RNA-seq data of the AICH-rats were analyzed for differential expression and functional enrichments. Herb-Compound-Target-Pathway (H-C-T-P) network was shown to clarify the mechanism of LXTYF for AICH. Results . 76 active ingredients (quercetin, Alanine, kaempferol, etc.) of LXTYF and 376 putative targets to alleviate AICH (PTGS2, PTGS1, ESR1, etc.) were successfully identified. The protein-protein interaction (PPI) network indicated the important role of STAT3. The functional enrichment of GO and KEGG pathway showed that LXTYF is most likely to influence MAPK and PI3K-Akt signaling pathways for AICH treatment. From the RNA-seq of AICH-rats, 583 differential mRNAs were identified and 14 of them were consistent with the putative targets of LXTYF for AICH treatment. The KEGG pathway enrichment also implied that the MAPK signaling pathway was the most correlated one among all the related signaling pathways. Many important targets with expression changes of LXTYF for AICH treatment and their related pathways are great markers of antioxidation, anti-inflammatory, antiapoptosis, and lowering blood pressure, which indicated that LXTYF may play mutiroles in the mechanisms for AICH treatment. Conclusion . The LXTYF attenuates AICH partially by antioxidation, anti-inflammatory, and antiapoptosis and lowers blood pressure roles through regulating the targets involved MAPK, calcium, apoptosis, and TNF signaling pathway, which provide notable clues for further experimental validation.

Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate and intuitive clustering results. Compared with two classical algorithms, namely, the k -means and density-based spatial clustering of applications with noise (DBSCAN) algorithms, as well as the unimproved original fast peak clustering algorithm, the proposed algorithm is faster and more accurate and can reveal the complex relationships among massive data more efficiently. During the process of reforming the toll system, the algorithm can automatically and more efficiently analyze massive toll data and detect abnormal events, thereby providing a theoretical basis and data support for the operation monitoring and maintenance of highways.

The regression problem is a valued problem in the domain of machine learning, and it has been widely employed in many fields such as meteorology, transportation, and material. Granular computing (GrC) is a good approach of exploring human intelligent information processing, which has the superiority of knowledge discovery. Ensemble learning is easy to execute parallelly. Based on granular computing and ensemble learning, we convert the regression problem into granular space equivalently to solve and proposed boosted fuzzy granular regression trees (BFGRT) to predict a test instance. The thought of BFGRT is as follows. First, a clustering algorithm with automatic optimization of clustering centers is presented. Next, in terms of the clustering algorithm, we employ MapReduce to parallelly implement fuzzy granulation of the data. Then, we design new operators and metrics of fuzzy granules to build fuzzy granular rule base. Finally, a fuzzy granular regression tree (FGRT) in the fuzzy granular space is presented. In the light of these, BFGRT can be designed by parallelly combing multiple FGRTs via random sampling attributes and MapReduce. Theory and experiments show that BFGRT is accurate, efficient, and robust.

In recent years, a series of researches have revealed that the Deep Neural Network (DNN) is vulnerable to adversarial attack, and a number of attack methods have been proposed. Among those methods, an extremely sly type of attack named the one-pixel attack can mislead DNNs to misclassify an image via only modifying one pixel of the image, leading to severe security threats to DNN-based information systems. Currently, no method can really detect the one-pixel attack, for which the blank will be filled by this paper. This paper proposes two detection methods, including trigger detection and candidate detection. The trigger detection method analyzes the vulnerability of DNN models and gives the most suspected pixel that is modified by the one-pixel attack. The candidate detection method identifies a set of most suspected pixels using a differential evolution-based heuristic algorithm. The real-data experiments show that the trigger detection method has a detection success rate of 9.1%, and the candidate detection method achieves a detection success rate of 30.1%, which can validate the effectiveness of our methods.

Hypertensive-induced renal damage (HRD) is an important public health and socioeconomic problem worldwide. The herb pair Radix Astragali - (RA-) Radix Salviae Miltiorrhizae (RS) is a common prescribed herbal formula for the treatment of HRD. However, the underlying mechanisms are unclear. The purpose of our study is to explore the mechanism of combination of Radix Astragali (RA) and Radix Salviae Miltiorrhizae (RS) ameliorating HRD by regulation of the renal sympathetic nerve. Thirty 24-week-old spontaneously hypertensive rats (SHRs) as the experimental group were randomly divided into the RA group, the RS group, the RA+RS group, the valsartan group, and the SHR group and six age-matched Wistar Kyoto rats (WKY) as the control group. After 4 weeks of corresponding drug administration, venipuncture was done to collect blood and prepare serum for analysis. A color Doppler ultrasound diagnostic instrument was used to observe renal hemodynamics. Enzyme-linked immunosorbent assay was used to detect norepinephrine (NE), epinephrine (E), angiotensin II (Ang II), and B-type brain natriuretic peptide (BNP). Simultaneously, the kidneys were removed immediately and observed under a transmission electron microscope to observe the ultrastructural changes. And the concentration of transforming growth factor- β 1 (TGF- β 1), angiotensin type 1 receptor (AT1), and nitric oxide (NO) was detected by immunohistochemistry. Our results showed that renal ultrasonography of rats showed no significant difference in renal size among groups. The RA+RS group had obviously decreased vascular resistance index. The levels of NE, E, BNP, Ang II, AT1, and TGF- β 1 were decreased ( ), and the density of NO was increased. Pathological damage of the kidney was alleviated. In conclusion, the results of the present study suggested sympathetic overexpression in the pathogenesis of HRD. The combination of RA and RS may inhibit the hyperexcitability of sympathetic nerves and maintain the normal physiological structure and function of kidney tissue and has a protective effect on the cardiovascular system.