Predicting Current Potential Distribution and the Range Dynamics of Pomacea canaliculata in China under Global Climate Change

Pomacea canaliculata is one of the 100 worst invasive alien species in the world, which has significant effects and harm to native species, ecological environment, human health, and social economy. Climate change is one of the major causes of species range shifts. With recent climate change, the distribution of P. canaliculata has shifted northward. Understanding the potential distribution under current and future climate conditions will aid in the management of the risk of its invasion and spread. Here, we used species distribution modeling (SDM) methods to predict the potential distribution of P. canaliculata in China, and the jackknife test was used to assess the importance of environmental variables for modeling. Our study found that precipitation of the warmest quarter and maximum temperature in the coldest months played important roles in the distribution of P. canaliculata. With global warming, there will be a trend of expansion and northward movement in the future. This study could provide recommendations for the management and prevention of snail invasion and expansion.

A Data Mining Method For Improving the Prediction Of Bioinformatics Data

The composition of proteins nearly correlated with its function. Therefore, it is very ungently important to discuss a method that can automatically forecast protein structure. The fusion encoding method of PseAA and DC was adopted to describe the protein features. Using this encoding method to express protein sequences will produce higher dimensional feature vectors. This paper uses the algorithm of predigesting the characteristic dimension of proteins. By extracting significant feature vectors from the primitive feature vectors, eigenvectors with high dimensions are changed to eigenvectors with low dimensions. The experimental method of jackknife test is adopted. The consequences indicate that the arithmetic put forwarded here is appropriate for identifying whether the given protein is a homo-oligomer or a hetero-oligomer.

Evaluating the Disaster Risk of the COVID-19 Pandemic Using an Ecological Niche Model

Since 2019, the novel coronavirus has spread rapidly worldwide, greatly affecting social stability and human health. Pandemic prevention has become China’s primary task in responding to the transmission of COVID-19. Risk mapping and the proposal and implementation of epidemic prevention measures emphasize many research efforts. In this study, we collected location information for confirmed COVID-19 cases in Beijing, Shenyang, Dalian, and Shijiazhuang from 5 October 2020 to 5 January 2021, and selected 15 environmental variables to construct a model that comprehensively considered the parameters affecting the outbreak and spread of COVID-19 epidemics. Annual average temperature, catering, medical facilities, and other variables were processed using ArcGIS 10.3 and classified into three groups, including natural environmental variables, positive socio-environmental variables, and benign socio-environmental variables. We modeled the epidemic risk distribution for each area using the MaxEnt model based on the case occurrence data and environmental variables in four regions, and evaluated the key environmental variables influencing the epidemic distribution. The results showed that medium-risk zones were mainly distributed in Changping and Shunyi in Beijing, while Huanggu District in Shenyang and the southern part of Jinzhou District and the eastern part of Ganjingzi District in Dalian also represented areas at moderate risk of epidemics. For Shijiazhuang, Xinle, Gaocheng and other places were key COVID-19 epidemic spread areas. The jackknife assessment results revealed that positive socio-environmental variables are the most important factors affecting the outbreak and spread of COVID-19. The average contribution rate of the seafood market was 21.12%, and this contribution reached as high as 61.3% in Shenyang. The comprehensive analysis showed that improved seafood market management, strengthened crowd control and information recording, industry-catered specifications, and well-trained employees have become urgently needed prevention strategies in different regions. The comprehensive analysis indicated that the niche model could be used to classify the epidemic risk and propose prevention and control strategies when combined with the assessment results of the jackknife test, thus providing a theoretical basis and information support for suppressing the spread of COVID-19 epidemics.

Predicting the Potential Distribution of Hylomecon japonica in China under Current and Future Climate Change Based on Maxent Model

Hylomecon japonica is considered a natural medicinal plant with anti-inflammatory, anticancer and antibacterial activity. The assessment of climate change impact on its habitat suitability is important for the wild cultivation and standardized planting of H. japonica. In this study, the maximum entropy model (Maxent) and geographic information system (ArcGIS) were applied to predict the current and future distribution of H. japonica species, and the contributions of variables were evaluated by using the jackknife test. The area under the receiver operating characteristic curve (AUC) value confirmed the accuracy of the model prediction based on 102 occurrence records. The predicted potential distributions of H. japonica were mainly concentrated in Jilin, Liaoning, Shaanxi, Chongqing, Henan, Heilongjiang and other provinces (adaptability index > 0.6). The jackknife experiment showed that the precipitation of driest month (40.5%), mean annual temperature (12.4%), the precipitation of wettest quarter (11.6%) and the subclass of soil (9.7%) were the most important factors affecting the potential distribution of H. japonica. In the future, only under the shared socioeconomic Pathway 245 (SSP 245) scenario model in 2061–2080, the suitable habitat area for H. japonica is expected to show a significant upward trend. The area under other scenarios may not increase or decrease significantly.

A Comparison of Efficiency for Homogeneity of Variance Tests under Log-normal Distribution

In this paper, we compare five homogeneity of variance tests which are Bartlett’s test, Levene’s test, Cochran’s test, O’Brien’s test and Jackknife test. Considering their ability to control probability of type I error and the power of the test, when groups of population are distributed in log-normal distribution. The equal sample sizes are defined as 10, 15, 30 and 50 at the significance is 0.05. The results show that the Levene’s test become the best test for the high skewed distribution. For the lowed skew distribution, the Cochran’s test is the best test when a variance of group is different to the others. Moreover, Bartlett’s test provides the highest power when variances of all populations are different.

Predicting Mango Sudden Decline Due to Ceratocystis fimbriata Under a Changing Climate

Mango fruit trees are an important fruit crop due to their high value. Mango sudden decline (MSD) is a major disease that threatens mango trees in Oman and worldwide. The objective of this study was to identify those areas in northern Oman in which Ceratocystis fimbriata (a plant fungal pathogen causing MSD) may establish itself under various climate change scenarios. The MaxEnt model used in this study was based on data for the period 1970-2000 and then projected to future climate periods. This study modeled the future distribution of C. fimbriata for 2021–2040, 2041–2060, 2061–2080, and 2081–2100 climatic scenarios. Fifteen affected locations and seven bioclimatic variables were investigated in this study. The model showed values between 0.896 and 0.913 (habitat suitability) which represented a good model outcome. The jackknife test showed that the mean diurnal range in temperature, precipitation of the driest month, and elevation contributed to C. fimbriata distribution. From 2021 through 2040, a total area of 1,889 km2 was found to be highly suitable for C. fimbriata in Northern Oman. Compared with the 2021–2040 period, the poorly suitable area would increase in both 2041–2060 and 2081–2100 periods. The moderately suitable regions for C. fimbriata would decrease under all scenarios investigated. However, the total area of the suitable areas, with all scenarios, would increase, except during the 2041-2060 period. This research offers a tool to better manage and prevent the possible Ceratocystis blight (C. fimbriata) and bark beetle (Hypocryphalus mangiferae) invasions under future projected climatic scenarios. Keywords: Mango sudden decline (MSD), “Ceratocystis fimbriata”, bioclimatic variables, climate change, Sultanate of Oman, Maxent.

Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis

Shallow landslides are destructive hazards and play an important role in landscape processes. The purpose of this paper is to evaluate the shallow landslide susceptibility and to investigate which predisposing factors control the spatial distribution of the collected instability phenomena. The GIS-based logistic regression model and jackknife test were respectively employed to achieve the scopes. The studied area falls in the Mesima basin, located in the southern Calabria (Italy). The research was based mainly on geomorphological study using both interpretation of Google Earth images and field surveys. Thus, 1511 shallow landslides were mapped and 18 predisposing factors (lithology, distance to faults, fault density, land use, soil texture, soil bulk density, soil erodibility, distance to streams, drainage density, elevation, slope gradient, slope aspect, local relief, plan curvature, profile curvature, TPI, TWI, and SPI) were recognized as influencing the shallow landslide susceptibility. The 70% of the collected shallow landslides were randomly divided into a training data set to build susceptibility model and the remaining 30% were used to validate the newly built model. The logistic regression model calculated the landslide probability of each pixel in the study area and produced the susceptibility map. Four classification methods were tested and compared between them, so the most reliable classification system was employed to the shallow landslide susceptibility map construction. In the susceptibility map, five classes were recognized as following: very low, low, moderate, high, and very high susceptibility. About 26.1% of the study area falls in high and very high susceptible classes and most of the landslides mapped (82.4%) occur in these classes. The accuracy of the predictive model was evaluated by using the ROC (receiver operating characteristics) curve approach, which showed an area under the curve (AUC) of 0.93, proving the excellent forecasting ability of the susceptibility model. The predisposing factors importance evaluation, using the jackknife test, revealed that slope gradient, TWI, soil texture and lithology were the most important factors; whereas, SPI, fault density and profile curvature have a least importance. According to these results, we conclude that the shallow landslide susceptibility map can be use as valuable tool both for land-use planning and for management and mitigation of the shallow landslide risk in the study area.

UbiSites-SRF: Ubiquitination Sites Prediction Using Statistical Moment with Random Forest Approach

Ubiquitination is the process that supports the growth and development of eukaryotic and prokaryotic organisms. It is helpful in regulating numerous functions such as the cell division cycle, caspase-mediated cell death, maintenance of protein transcription, signal transduction, and restoration of DNA damage. Because of these properties, its identification is essential to understand its molecular mechanism. Some traditional methods such as mass spectrometry and site-directed mutagenesis are used for this purpose, but they are tedious and time consuming. In order to overcome such limitations, interest in computational models of this type of identification is therefore being developed. In this study, an accurate and efficient classification model for identifying ubiquitination sites was constructed. The proposed model uses statistical moments for feature extraction along with random forest for classification. Three sets of ubiquitination are used to train and test the model. The model is assessed through 10-fold cross-validation and jackknife tests. We achieved a 10-fold accuracy of 100% for dataset-1, 99.88% for dataset-2 and 99.84% for the dataset-3, while with Jackknife test we got 100% for the dataset-1, 99.91% for dataset-2 and 99.99%. for the dataset-3. The results obtained are almost the maximum, which is far better as compared to the pre-existing models available in the literature.

Prediction of Potential Geographical Distribution Patterns of Actinidia arguta under Different Climate Scenarios

Actinidia arguta (Siebold and Zucc.) Planch.ex Miq, called “hardy kiwifruit”, “baby kiwi” or “kiwi berry”, has a unique taste, is rich in nutrients and has high economic value and broad market prospects. Active research on the potential geographic distribution of A. arguta in China aims to provide a reference basis for its resource investigation, conservation, development and utilization and introduction of cultivation. In this study, the Maxent model was used to combine climatic factors, soil factors and geographical factors (elevation, slope and aspect) to predict the current and future (2041–2060 and 2081–2100) potential distribution of A. arguta and to analyze the impact of climate change on it. The results showed that the suitable distribution range of A. arguta in China was 23–43 N and 100–125 E, with a total area of about 3.4451 × 106 km2. The highly suitable area of A. arguta was mainly concentrated in the middle and low mountain areas of the south of Shaanxi, the east of Sichuan, the middle and west of Guizhou and the west of Yunnan, presenting a circular distribution. The Jackknife test was used to calculate the main environmental factors affecting the distribution of A. arguta. The first four main factors were annual mean temperature (bio_1), precipitation of the warmest quarter (bio_18), elevation (ELE) and mean temperature of the warmest quarter (bio_10), which provided a contribution up to 81.7%. Under the scenarios of three representative concentrations (SSP1_2.6, SSP2_4.5 and SSP5_8.5) in the future, the area of low and moderate suitable habitat decreased, while the area of highly suitable habitat increased. The migration direction of the centroid in the highly suitable habitat moved to the southwest in the future scenario period.

Climatic-niche evolution with key morphological innovations across clades within Scutiger boulengeri (Anura: Megophryidae)

The studies of climatic-niche shifts over evolutionary time accompanied by key morphological innovations have attracted the interest of many researchers recently. We analyzed the realized niche dynamics across clades within Scutiger boulengeri using ecological niche models (ENMs), ordination method (environment principal component analysis; PCA-env), and correspondingly key morphological innovations combined phylogenetic comparative methods (PCMs) and phylogenetic generalized least squares (PGLS) regression methods throughout their distributions in Qinghai-Tibet Plateau (QTP) margins of China in Asia. Our analyses reveal that there is obvious niche divergence caused by niche expansion across S. boulengeri clades, especially in E. A, E. B and partial of E. C clades. Moreover, niche expansion is more popular than niche unfilling into novel environmental conditions. Annual mean temperature and Annual precipitation are the most important contributors in E. A and E. B clades, while Precipitation of driest month is most likely to be the leading limited factor in these two regions according to jackknife test of variable importance. In addition, we identified several key ecological and morphological traits that tend to be associated with niche expansion in S. boulengeri clades correspondingly. Specifically, we found that Elevation, Isothermality, Mean diurnal range and Max temperature of warmest month are significantly negative predictors of snout–vent length (SVL) under phylogenetic models, while the S. boulengeri toads from warmer and more arid environments tend to be larger. There seems to a trade-off strategy by trait covary of locomotor performance combined with enlarged SVL, which provide us a potential pattern of how a colonizing toad might seed a novel habit to begin the process of speciation and finally adaptive radiation. It is worth noting that we should not overlook that the Tibet continuously growing and moving northward over millions of years has laid the foundation for early divergence of clades within S. boulengeri.