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  1. Ng WM, Che HX, Guo C, Liu C, Low SC, Chieh Chan DJ, et al.
    Langmuir, 2018 07 10;34(27):7971-7980.
    PMID: 29882671 DOI: 10.1021/acs.langmuir.8b01210
    An artificial magnetotactic microbot was created by integrating the microalgal cell with magnetic microbead for its potential application as biomotor in microscale environment. Here, we demonstrate the remote magnetotactic control of the microbot under a low gradient magnetic field (<100 T/m). We characterize the kinematic behavior of the microbots carrying magnetic microbeads of two different sizes, with diameter of 2 and 4.5 μm, in the absence and presence of magnetic field. In the absence of magnetic field, we observed the microbot showed a helical motion as a result of the misalignment between the thrust force and the symmetry axis after the attachment. The microbot bound with a larger magnetic microbead moved with higher translational velocity but rotated slower about its axis of rotation. The viscous force was balanced by the thrust force of the microbot, resulting in a randomized swimming behavior of the microbot at its terminal velocity. Meanwhile, under the influence of a low gradient magnetic field, we demonstrated that the directional control of the microbot was based on following principles: (1) magnetophoretic force was insignificant on influencing its perpendicular motion and (2) its parallel motion was dependent on both self-swimming and magnetophoresis, in which this cooperative effect was a function of separation distance from the magnet. As the microbot approached the magnet, the magnetophoretic force suppressed its self-swimming behavior, leading to a positive magnetotaxis of the microbot toward the source of magnetic field. Our experimental results and kinematic analysis revealed the contribution of mass density variation of particle-and-cell system on influencing its dynamical behavior.
  2. Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, et al.
    Sci. Total Environ., 2018 Sep 01;634:853-867.
    PMID: 29653429 DOI: 10.1016/j.scitotenv.2018.04.055
    The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.
  3. Guo C, Zhou Z, Wen Z, Liu Y, Zeng C, Xiao D, et al.
    PMID: 28748176 DOI: 10.3389/fcimb.2017.00317
    Dengue is an arthropod-borne infectious disease caused by dengue virus (DENV) infection and transmitted byAedesmosquitoes. Approximately 50-100 million people are infected with DENV each year, resulting in a high economic burden on both governments and individuals. Here, we conducted a systematic review and meta-analysis to summarize information regarding the epidemiology, clinical characteristics, and serotype distribution and risk factors for global dengue outbreaks occurring from 1990 to 2015. We searched the PubMed, Embase and Web of Science databases through December 2016 using the term "dengue outbreak." In total, 3,853 studies were identified, of which 243 studies describing 262 dengue outbreaks met our inclusion criteria. The majority of outbreak-associated dengue cases were reported in the Western Pacific Region, particularly after the year 2010; these cases were primarily identified in China, Singapore and Malaysia. The pooled mean age of dengue-infected individuals was 30.1 years; of the included patients, 54.5% were male, 23.2% had DHF, 62.0% had secondary infections, and 1.3% died. The mean age of dengue patients reported after 2010 was older than that of patients reported before 2010 (34.0 vs. 27.2 years); however, the proportions of patients who had DHF, had secondary infections and died significantly decreased after 2010. Fever, malaise, headache, and asthenia were the most frequently reported clinical symptoms and signs among dengue patients. In addition, among the identified clinical symptoms and signs, positive tourniquet test (OR= 4.86), ascites (OR= 13.91) and shock (OR= 308.09) were identified as the best predictors of dengue infection, DHF and mortality, respectively (bothP< 0.05). The main risk factors for dengue infection, DHF and mortality were living with uncovered water container (OR= 1.65), suffering from hypotension (OR= 6.18) and suffering from diabetes mellitus (OR= 2.53), respectively (allP< 0.05). The serotype distribution varied with time and across WHO regions. Overall, co-infections were reported in 47.7% of the evaluated outbreaks, and the highest pooled mortality rate (2.0%) was identified in DENV-2 dominated outbreaks. Our study emphasizes the necessity of implementing programs focused on targeted prevention, early identification, and effective treatment.
  4. Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.
    Autophagy, 2016;12(1):1-222.
    PMID: 26799652 DOI: 10.1080/15548627.2015.1100356
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