Hypervolume Niche Dynamics and Global Invasion Risk of Phenacoccus solenopsis under Climate Change.

As a globally invasive quarantine pest, the cotton mealybug, Phenacoccus solenopsis, is spreading rapidly, posing serious threats against agricultural and forestry production and biosecurity. In recent years, the niche conservatism hypothesis has been widely debated, which is particularly evident in invasive biology research. Identifying the niche dynamics of P. solenopsis, as well as assessing its global invasion risk, is of both theoretical and practical importance. Based on 462 occurrence points and 19 bioclimatic variables, we used n-dimensional hypervolume analysis to quantify the multidimensional climatic niche of this pest in both its native and invasive ranges. We examined niche conservatism and further optimized the MaxEnt model parameters to predict the global invasion risk of P. solenopsis under both current and future climate conditions. Our findings indicated that the niche hypervolume of this pest in invasive ranges was significantly larger than that in its native ranges, with 99.45% of the niche differentiation contributed by niche expansion, with the remaining less than 1% explained by space replacement. Niche expansion was most evident in Oceania and Eurasia. The area under the receiver operating characteristic curve (0.83) and true skill statistic (0.62) indicated the model's robust performance. The areas of suitable habitats for P. solenopsis are increasing significantly and the northward spread is obvious in future climate change scenarios. North Africa, northern China, Mediterranean regions, and northern Europe had an increased invasion risk of P. solenopsis. This study provided scientific support for the early warning and control of P. solenopsis.

Multidimensional climatic niche conservatism and invasion risk of Phenacoccus solenopsis.

The cotton mealybug Phenacoccus solenopsis, a globally invasive insect, is listed as a national quarantine pest in agriculture and forestry, which seriously threatens biological safety of China. Niche conservatism is a key assumption of species distribution model. An evaluation of the applicability of using ecological niche models to assess the invasion risk of cotton mealybug, and further optimizing model complexity, are of both theoretical and practical significance. Based on 706 occurrence records and key bioclimatic variables, we used n-dimensional hypervolume niche analysis method to quantify the climatic niche hypervolumes of this pest in both native and invasive sites, and further tested the niche conservatism hypothesis. MaxEnt model parameters were optimized to predict the invasion risk of the mealybug under current and future climate scenarios in China. The results showed that four climatic variables (annual mean temperature, mean temperature of wettest quarter, mean temperature of warmest quarter, and precipitation of driest quarter) were the key climate factors affecting the distribution of cotton mealybug. Compared with native climatic niche (hypervolume volume, HV=40.43), the niche hypervolume of cotton mealybug in the invasive areas was significantly reduced (HV=6.04). Niche contraction (the net differences between the amount of space enclosed by each hypervolume was 0.84) explained 98.8% of niche differentiation, whereas niche shift (the replacement of space between hypervolumes was 0.01) contributed less than 2%. The direction of climatic niche contraction of the pest in different invasive areas was not exactly consistent. The default parameters of MaxEnt model were unreliable (ΔAICc=14.27), and the optimal parameter combination was obtained as follows: feature combination was linear-quadratic-hinge-product and regularization multiplier was 0.5. The most suitable habitats of cotton mealybug were concentrated in the south of Huaihe River-Qinling Mountains line, and the north-central provinces contained a large area of low suitable habitat. The increase of suitable habitat was not significant at the end of 21 century (SSP1-2.6: 1.7%, SSP5-8.5: 0.7%). The multidimensional climatic niche of P. solenopsis was highly conservative. The species distribution model was suitable for analyzing its invasion risk. The northward spread was obvious, and climate change had less impact on the pest.

Adaptive Federated IMM Filter for AUV Integrated Navigation Systems.

High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.

An Improved Adaptive Compensation H∞ Filtering Method for the SINS' Transfer Alignment Under a Complex Dynamic Environment.

Transfer alignment on a moving base under a complex dynamic environment is one of the toughest challenges in a strapdown inertial navigation system (SINS). With the aim of improving rapidity and accuracy, velocity plus attitude matching is applied in the transfer alignment model. Meanwhile, the error compensation model is established to calibrate and compensate the errors of inertial sensors online. To suppress the filtering divergence during the process of transfer alignment, this paper proposes an improved adaptive compensation H∞ filtering method. The cause of filtering divergence has been analyzed carefully and the corresponding adjustment and optimization have been made in the proposed adaptive compensation H∞ filter. In order to balance accuracy and robustness of the transfer alignment system, the robustness factor of the adaptive compensation H∞ filter can be dynamically adjusted according to the complex external environment. The aerial transfer alignment experiments illustrate that the adaptive compensation H∞ filter can effectively improve the transfer alignment accuracy and the pure inertial navigation accuracy under a complex dynamic environment, which verifies the advantage of the proposed method.

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems.

Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method.

The preventive effect of vaccine prophylaxis on severe respiratory syncytial virus infection: A meta-analysis.

Respiratory syncytial virus (RSV) is the key underlying cause of acute lower respiratory tract infection in infants; however, no licensed vaccine against RSV infection is currently available. This study was undertaken to assess the preventive effect of vaccine on RSV infection. In this metaanalysis, 1,792 published randomized clinical trials of RSV vaccines from Jan 1973 to Sep 2015 were examined. Among thirteen studies that met the inclusion criteria, eleven studies estimated the impact of RSV vaccines and four studies estimated the effect of adjuvants. The odds ratios (ORs) were 0.31 (95% CI, 0.15-0.67) and 0.62 (95% CI, 0.29-1.34), respectively. We found that RSV subunit vaccines can significantly reduce the incidence of RSV infection and that whether vaccination with adjuvant therapy was an effective strategy still remained to be studied. This analysis of the preventive effect of vaccines on RSV infection has direct applications for the prevention of RSV infections.