Spatial functional prediction from spatial autoregressive Hilbertian processes

2011 ◽  
Vol 23 (1) ◽  
pp. 119-128 ◽  
Author(s):  
M. D. Ruiz-Medina
Keyword(s):  
Functional Prediction ◽  
Spatial Autoregressive ◽  
Autoregressive Hilbertian Processes

scholarly journals Analysis of factors causing the number of poor people in DKI Jakarta using spatial autoregressive Poisson model

2021 ◽  
Author(s):  
Siti Rohmah Rohimah ◽  
Qorry Meidianingsih ◽  
Nabilah Ninda Nur Azizah ◽  
Ahmad Syauqy Baihaqy
Keyword(s):  
Poisson Model ◽  
Poor People ◽  
Spatial Autoregressive

scholarly journals Data-driven biological network alignment that uses topological, sequence, and functional information

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shawn Gu ◽  
Tijana Milenković
Keyword(s):  
Prediction Accuracy ◽  
Biological Network ◽  
Network Alignment ◽  
Data Driven ◽  
Sequence Information ◽  
Functional Prediction ◽  
Research Knowledge ◽  
Topological Information ◽  
Functional Knowledge ◽  
Related Proteins

Abstract Background Network alignment (NA) can transfer functional knowledge between species’ conserved biological network regions. Traditional NA assumes that it is topological similarity (isomorphic-like matching) between network regions that corresponds to the regions’ functional relatedness. However, we recently found that functionally unrelated proteins are as topologically similar as functionally related proteins. So, we redefined NA as a data-driven method called TARA, which learns from network and protein functional data what kind of topological relatedness (rather than similarity) between proteins corresponds to their functional relatedness. TARA used topological information (within each network) but not sequence information (between proteins across networks). Yet, TARA yielded higher protein functional prediction accuracy than existing NA methods, even those that used both topological and sequence information. Results Here, we propose TARA++ that is also data-driven, like TARA and unlike other existing methods, but that uses across-network sequence information on top of within-network topological information, unlike TARA. To deal with the within-and-across-network analysis, we adapt social network embedding to the problem of biological NA. TARA++ outperforms protein functional prediction accuracy of existing methods. Conclusions As such, combining research knowledge from different domains is promising. Overall, improvements in protein functional prediction have biomedical implications, for example allowing researchers to better understand how cancer progresses or how humans age.

scholarly journals Interactive effects between travel behaviour and COVID-19: a questionnaire study

2021 ◽  
Author(s):  
Jinbao Zhang ◽  
Jaeyoung Lee
Keyword(s):  
Early Stage ◽  
Travel Behaviour ◽  
Interactive Effects ◽  
Factors Affecting ◽  
Individual Level ◽  
Passenger Volume ◽  
Spatial Autoregressive ◽  
Railway Passenger ◽  
The Individual ◽  
And Control

Abstract This study has two main objectives: (i) to analyse the effect of travel characteristics on the spreading of disease, and (ii) to determine the effect of COVID-19 on travel behaviour at the individual level. First, the study analyses the effect of passenger volume and the proportions of different modes of travel on the spread of COVID-19 in the early stage. The developed spatial autoregressive model shows that total passenger volume and proportions of air and railway passenger volumes are positively associated with the cumulative confirmed cases. Second, a questionnaire is analysed to determine changes in travel behaviour after COVID-19. The results indicate that the number of total trips considerably decreased. Public transport usage decreased by 20.5%, while private car usage increased by 6.4%. Then the factors affecting the changes in travel behaviour are analysed by logit models. The findings reveal significant factors, including gender, occupation and travel restriction. It is expected that the findings from this study would be helpful for management and control of traffic during a pandemic.

scholarly journals Seasonal Succession of Bacterial Communities in Three Eutrophic Freshwater Lakes

2021 ◽  
Vol 18 (13) ◽  
pp. 6950
Author(s):  
Bin Ji ◽  
Cheng Liu ◽  
Jiechao Liang ◽  
Jian Wang
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Temperature ◽  
Bacterial Communities ◽  
Seasonal Succession ◽  
Lake Eutrophication ◽  
Urban Lake ◽  
Freshwater Lakes ◽  
Functional Prediction ◽  
Bacterial Indicator

Urban freshwater lakes play an indispensable role in maintaining the urban environment and are suffering great threats of eutrophication. Until now, little has been known about the seasonal bacterial communities of the surface water of adjacent freshwater urban lakes. This study reported the bacterial communities of three adjacent freshwater lakes (i.e., Tangxun Lake, Yezhi Lake and Nan Lake) during the alternation of seasons. Nan Lake had the best water quality among the three lakes as reflected by the bacterial eutrophic index (BEI), bacterial indicator (Luteolibacter) and functional prediction analysis. It was found that Alphaproteobacteria had the lowest abundance in summer and the highest abundance in winter. Bacteroidetes had the lowest abundance in winter, while Planctomycetes had the highest abundance in summer. N/P ratio appeared to have some relationships with eutrophication. Tangxun Lake and Nan Lake with higher average N/P ratios (e.g., N/P = 20) tended to have a higher BEI in summer at a water temperature of 27 °C, while Yezhi Lake with a relatively lower average N/P ratio (e.g., N/P = 14) tended to have a higher BEI in spring and autumn at a water temperature of 9–20 °C. BEI and water temperature were identified as the key parameters in determining the bacterial communities of lake water. Phosphorus seemed to have slightly more impact on the bacterial communities than nitrogen. It is expected that this study will help to gain more knowledge on urban lake eutrophication.

scholarly journals Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1448
Author(s):  
Xuan Liu ◽  
Jianbao Chen
Keyword(s):  
Variable Selection ◽  
Autoregressive Model ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Spatial Autoregressive Model ◽  
Monte Carlo Studies ◽  
Spatial Autoregressive ◽  
Theoretical Results

Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.

scholarly journals PREVALENCE OF ANTIBODIES AGAINST INFLUENZA VIRUS IN NON-VACCINATED EQUINES FROM THE BRAZILIAN PANTANAL

2014 ◽  
Vol 56 (6) ◽  
pp. 487-492 ◽  
Author(s):  
Lucas Gaíva E Silva ◽  
Alice Mamede Costa Marques Borges ◽  
Eliana Monteforte Cassaro Villalobos ◽  
Maria do Carmo Custodio Souza Hunold Lara ◽  
Elenice Maria Siquetin Cunha ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Equine Infectious Anemia Virus ◽  
Linear Regression Models ◽  
Equine Influenza Virus ◽  
Chi Square ◽  
Equine Influenza ◽  
Hemagglutination Inhibition Test ◽  
Chi Square Test ◽  
Spatial Autoregressive ◽  
Anemia Virus

The prevalence of antibodies against Equine Influenza Virus (EIV) was determined in 529 equines living on ranches in the municipality of Poconé, Pantanal area of Brazil, by means of the hemagglutination inhibition test, using subtype H3N8 as antigen. The distribution and possible association among positive animal and ranches were evaluated by the chi-square test, spatial autoregressive and multiple linear regression models. The prevalence of antibodies against EIV was estimated at 45.2% (95% CI 30.2 - 61.1%) with titers ranging from 20 to 1,280 HAU. Seropositive equines were found on 92.0% of the surveyed ranches. Equine from non-flooded ranches (66.5%) and negativity in equine infectious anemia virus (EIAV) (61.7%) were associated with antibodies against EIV. No spatial correlation was found among the ranches, but the ones located in non-flooded areas were associated with antibodies against EIV. A negative correlation was found between the prevalence of antibodies against EIV and the presence of EIAV positive animals on the ranches. The high prevalence of antibodies against EIV detected in this study suggests that the virus is circulating among the animals, and this statistical analysis indicates that the movement and aggregation of animals are factors associated to the transmission of the virus in the region.

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