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Iran is located in an arid and semi-arid region, where a large part of its water needs is provided by underground water sources. In severe and prolonged periods of drought when it is not possible to supply water from surface water sources; Groundwater will be the only source of water supply. Therefore, it is very important to investigate the status of these resources and determine the influencing factors on them. In this research, the areas that are exposed to severe groundwater drought were identified using the GRI groundwater drought index, and in order to measure the potential of drought-prone areas, 70 percent of the drought points were randomly selected for training purposes. model and 30% were selected to validate the results of the model. The influence of factors affecting groundwater drought such as distance from waterways, vegetation cover, height, slope, slope direction, topographic moisture index (TWI), soil surface moisture, rainfall It was determined by using the maximum entropy model and using MAXENT software in the watershed of Jiroft Plain. The results of this study based on the Jackknife test showed that the most important factors affecting groundwater drought are height, distance from the river and moisture of the soil surface and the model showed the most sensitivity to these parameters. The value of the area under the relative performance recognition curve (ROC) is 0.85 and 0.76 respectively for the samples Educational and experimental shows the acceptable level of results. [ABSTRACT FROM AUTHOR]

Protein functions are closely related to their subcellular locations. At present, the prediction of protein subcellular locations is one of the most important problems in protein science. The evident defects of traditional methods make it urgent to design methods with high efficiency and low costs. To date, lots of computational methods have been proposed. However, this problem is far from being completely solved. Recently, some multi-label classifiers have been proposed to identify subcellular locations of human, animal, Gram-negative bacterial and eukaryotic proteins. These classifiers adopted the protein features derived from gene ontology information. Although they provided good performance, they can be further improved by adopting more powerful machine learning algorithms. In this study, four improved multi-label classifiers were set up for identification of subcellular locations of the above four protein types. The random k-labelsets (RAKEL) algorithm was used to tackle proteins with multiple locations, and random forest was used as the basic prediction engine. All classifiers were tested by jackknife test, indicating their high performance. Comparisons with previous classifiers further confirmed the superiority of the proposed classifiers.

Flooding is a natural but unavoidable disaster that occurs over time. Flooding threatens human life, property, and resources and affects regional and national economies. Through frequency ratio and MaxEnt modeling, flood sensitivity was determined in the Amu Darya River Basin in Badakhshan Province, Afghanistan. Slope, plan curvature, distance to river, rainfall, aspect, land use, elevation, Normalized Difference Vegetation Index (NDVI), soil type, lithology, Topographic Humidity Index (TWI), and drainage density were used to quantify flood susceptibility. In total, 88 flood points collected from Google Earth were used to train the frequency ratio model to predict flood susceptibility, and 34 GPS-recorded points of the flooded area were used to evaluate the model's performance. The frequency ratio model displayed a success rate of above 86%. However, using a jackknife entropy test, the MaxEnt model yielded a 97% success rate. The results showed that rainfall, land use, distance to river, and soil type were the most important parameters for evaluating flood sensitivity. The developed models can help planners and decision-makers perform flood susceptibility mapping in the region by determining locations of flooding sensitivity. [ABSTRACT FROM AUTHOR]

Abstract Background and Aim Dengue fever, transmitted by Aedes mosquitoes, is a significant public health concern in tropical and subtropical regions. With the end of the COVID-19 pandemic and the reopening of the borders, dengue fever remains a threat to mainland China, Zhejiang province of China is facing a huge risk of importing the dengue virus. This study aims to analyze and predict the current and future potential risk regions for Aedes vectors distribution and dengue prevalence in Zhejiang province of China. Method We collected occurrence records of DENV and DENV vectors globally from 2010 to 2022, along with historical and future climate data and human population density data. In order to predict the probability of DENV distribution in Zhejiang province of China under future conditions, the ecological niche of Ae. aegypti and Ae. albopictus was first performed with historical climate data based on MaxEnt. Then, predicted results along with a set of bioclimatic variables, elevation and human population density were included in MaxEnt model to analyze the risk region of DENV in Zhejiang province. Finally, the established model was utilized to predict the spatial pattern of DENV risk in the current and future scenarios in Zhejiang province of China. Results Our findings indicated that approximately 89.2% (90,805.6 KM2) of Zhejiang province of China is under risk, within about 8.0% (8,144 KM2) classified as high risk area for DENV prevalence. Ae. albopictus were identified as the primary factor influencing the distribution of DENV. Future predictions suggest that sustainable and “green” development pathways may increase the risk of DENV prevalence in Zhejiang province of China. Conversely, Fossil-fueled development pathways may reduce the risk due to the unsuitable environment for vectors. Conclusions The implications of this research highlight the need for effective vector control measures, community engagement, health education, and environmental initiatives to mitigate the potential spread of dengue fever in high-risk regions of Zhejiang province of China.

Background and Aim: Dengue fever, transmitted by Aedes mosquitoes, is a significant public health concern in tropical and subtropical regions. With the end of the COVID-19 pandemic and the reopening of the borders, dengue fever remains a threat to mainland China, Zhejiang province of China is facing a huge risk of importing the dengue virus. This study aims to analyze and predict the current and future potential risk regions for Aedes vectors distribution and dengue prevalence in Zhejiang province of China. Method: We collected occurrence records of DENV and DENV vectors globally from 2010 to 2022, along with historical and future climate data and human population density data. In order to predict the probability of DENV distribution in Zhejiang province of China under future conditions, the ecological niche of Ae. aegypti and Ae. albopictus was first performed with historical climate data based on MaxEnt. Then, predicted results along with a set of bioclimatic variables, elevation and human population density were included in MaxEnt model to analyze the risk region of DENV in Zhejiang province. Finally, the established model was utilized to predict the spatial pattern of DENV risk in the current and future scenarios in Zhejiang province of China. Results: Our findings indicated that approximately 89.2% (90,805.6 KM2) of Zhejiang province of China is under risk, within about 8.0% (8,144 KM2) classified as high risk area for DENV prevalence. Ae. albopictus were identified as the primary factor influencing the distribution of DENV. Future predictions suggest that sustainable and "green" development pathways may increase the risk of DENV prevalence in Zhejiang province of China. Conversely, Fossil-fueled development pathways may reduce the risk due to the unsuitable environment for vectors. Conclusions: The implications of this research highlight the need for effective vector control measures, community engagement, health education, and environmental initiatives to mitigate the potential spread of dengue fever in high-risk regions of Zhejiang province of China. [ABSTRACT FROM AUTHOR]

Carum carvi is a native species that is widely used in various medicinal, food, health, and cosmetic industries. In this study, the MaxEnt model was used to investigate the ecological factors affecting the establishment and distribution of Carum carvi and to prepare a map of its habitat suitability in Alamout Sharghi Rangelands (2019). To model the map, 19 environmental variables and information on the presence points of the species were prepared and entered into MAXENT software. Evaluation of the model results was obtained at 95% using AUC, which indicates the high accuracy of the model results. According to the results, C. carvi species prefers to grow in the altitude range of 1000 to 2700 meters, with lime of soil 14 to 25 percent, rainfall 400 to 500 mm, soil acidity 7.7-7.8, sand of soil about 35 percent, clay of soil 20 to 40 percent in Alamout Sharghi region. The probability of the presence of this species increases to 80% with the increase of soil organic matter from 0.5% to 2% and the probability of the presence of C. carvi species reaches 100% with the increase of silt of soil from 4 to 25%. Among the micronutrients, C. carvi showed a better response to increasing potassium. The variables of range direction, soil acidity, and sodium (Bio2, Bio15, and Bio14, respectively) were the least important environmental variables in the jackknife test. This basin has almost all geographical directions due to its mountainous nature. The acidity of the soil in this basin is 7.6-7.8 (almost neutral) and the amount of sodium in the soil is also negligible. The results of this study are used for domestication and cultivation of this plant in lands with similar habitat conditions for its widespread use and commercialization of its products. [ABSTRACT FROM AUTHOR]

Introduction: Outer membrane proteins are crucial in maintaining the structural stability and permeability of the outer membrane. Outer membrane proteins exhibit several functions such as antigenicity and strong immunogenicity, which have potential applications in clinical diagnosis and disease prevention. However, wet experiments for studying OMPs are time and capital-intensive, thereby necessitating the use of computational methods for their identification. Methods: In this study, we developed a computational model to predict outer membrane proteins. The non-redundant dataset consists of a positive set of 208 outer membrane proteins and a negative set of 876 non-outer membrane proteins. In this study, we employed the pseudo amino acid composition method to extract feature vectors and subsequently utilized the support vector machine for prediction. Results and Discussion: In the Jackknife cross-validation, the overall accuracy and the area under receiver operating characteristic curve were observed to be 93.19% and 0.966, respectively. These results demonstrate that our model can produce accurate predictions, and could serve as a valuable guide for experimental research on outer membrane proteins. [ABSTRACT FROM AUTHOR]

Kadsura coccinea, a traditional Chinese medicine, is widely used in folk medicine for the treatment of stomach disorders, rheumatoid arthritis, bruises and contusions, and gynecological disorders. We used 132 K. coccinea loci and 19 climatic variables, maximum entropy model (Maxent), and geographic information system (ArcGIS) technique to screen the dominant climatic factors and to delineate suitable growing areas for K. coccinea. The results show that the Maxent model predicts well (AUC > 0.9). This indicates that the study has very high accuracy and reliability. The most suitable habitat parameters for K. coccinea are the precipitation of the wettest quarter (723.2~968.4 mm), annual precipitation (1512.7~1991.4 mm; 3035.4~4680.4 mm), temperature annual range (8.3~23.2 1; 24.8~27.3 1), precipitation of the driest quarter (94.5~633.6 mm), precipitation of the warmest quarter (567.3~977.9 mm), mean diurnal range (3.5~7.7 1), precipitation of the driest month (38.2~199.1 mm), precipitation of the coldest quarter (182.9~633.6 mm). The most suitable distribution areas for K. coccinea are concentrated in tropical and subtropical regions, which are Guangxi, Hunan, Yunnan, Jiangxi, Guangdong, Hainan, southeastern Chongqing, southwestern Hubei, southern and southwestern Anhui, Guizhou, Zhejiang, Taiwan, Fujian, and Hong Kong. The results of the study can provide a theoretical basis for the exploitation, cultivation, and quality of wild K. coccinea resources. [ABSTRACT FROM AUTHOR]

Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied. Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins' subcellular location. Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved. [ABSTRACT FROM AUTHOR]

Mus macedonicus Petrov & Ružic, 1983, also known as the Macedonian mouse or Balkan short-tailed mouse, lives in the southern Balkans and the Middle East. While this species is common in Mediterranean ecosystems and is listed as "least concern" by the IUCN, little is known about how its distribution may shift with climate change. This study explores the 'species' potential distribution in three different periods: during the Last Glacial Maximum (LGM) and under current and future climate scenarios, using Maximum Entropy modelling. Modelling was based on 137 georeferenced occurrence records from Macedonia, Bulgaria, Greece, Turkey, Syria, Lebanon, Israel, Iran, Georgia, and Russia and ten bioclimatic variables from the WorldClim database. We show that a combination of precipitation and temperature variables appear to shape the geographical range of the Macedonian mouse and that its predicted distribution during the LGM is consistent with its survival in multiple refugia, as suggested by previous genetic studies. Modelled future distributions are subtly but significantly different from the current, with population losses and gains in different regions. Our results provide a sound framework for future studies on this model species' range dynamics, suggesting that the overall geographical range of M. macedonicus is relatively stable in the long term. [ABSTRACT FROM AUTHOR]

N6-methyladenine (6mA) is a type of post-replication modification that exists across a wide range of DNA sequences. It has emerged as a key element in various biological processes, and due to this fact, has attracted great attention for its ability to be a target for disease treatment. Identifying 6mA sites has been the prelude to understand multiple biological functions including DNA replication, transcription, and repairing. Aside from a few numbers of experimental methods, a series of predictors were developed to distinguish 6mA sites in the whole genome. In this study, a 6mA site predictor named iDNA6mA-Pred was designed, which used a variety of valid feature descriptors to obtain informative characteristics. To improve computational efficiency and reduce the amount of redundant information, 34-dimensional features were selected with the aid of the F-score and were employed to learn support vector machine model. This study resulted an support vector machine (SVM)-based predictor to recognize 6mA sites of the rice genome, where nucleic acid composition (NAC), dinucleotide Composition (DNC), and Position-Specific Trinucleotide Propensity (PSTNP) were used to characterize the DNA sequences. Jackknife test results showed that the property of iDNA6mA-Pred was accurate with an accuracy rate of 92.16%. This predictor could be used for accurate identification of 6mA sites. [ABSTRACT FROM AUTHOR]

Hormone-binding proteins (HBPs) are soluble carrier proteins that play a vital role in the growth and development of living organisms. Identifying HBPs accurately is crucial for understanding their functions. However, traditional wet lab experimental methods are labor intensive and cost ineffective. Therefore, there is a need for computational methods to efficiently identify HBPs. In this study, a machine learning method based on support vector machine (SVM) was proposed for the accurate and efficient identification of HBPs. The encoding of protein sequences involved using fifty different physicochemical (PC) properties. A variable-length window-based dynamic connectivity method was applied to capture the connection information between two different PC properties through two distinct strategies. The canonical correlation analysis algorithm was then used to fuse features obtained from these approaches. Feature selection was performed using the F-score approach to choose the most discriminative features. Finally, these selected features were fed into the SVM to discriminate between HBPs and non-HBPs. The proposed method achieved high classification accuracies of 99.19%, 96.77%, and 94.57% on the main dataset and two independent datasets, respectively, as demonstrated in the jackknife test. Comparative results showed that our proposed method outperforms existing approaches on the same datasets, indicating its potential as a useful tool for identifying HBPs. The Matlab codes and datasets used in the current study are freely available at https://figshare.com/articles/online_resource/iHBPs-VWDC/23559834.Communicated by Ramaswamy H. Sarma.

Species distribution models (SDMs), such as Maxent, are often used to estimate the occurrence of species. This useful tool was created to integrate the observed abundance/occurrence of species and environmental predictors to predict species distributions and environmental suitability across geographic space and the effects of climatic factors on populations of different bird species. It is believed that topography, bioclimatic factors, and forest structure all had a significant impact on the species probability maps created using the SDMs. In this study, the distribution of Himalayan woodpeckers (Dendrocopos himalayensis) in the Chamoli District of the state of Uttarakhand is attempted to be examined. During the analysis of the data, only 59 of the 200 total sites were still usable. The area under the curve (AUC) score, which was acquired, validates the anticipated model's accuracy and is equal to 0.897. Linear/quadratic/product regularisation values were 0.167, categorical was 0.250, threshold was 1.410, and the hinge was 0.500. By using the Jackknife test to determine how bioclimatic factors affect species' habitats, it was found that the decline in the habitat of the Himalayan woodpecker was caused by an increase in temperature and variability in the region's wettest month's precipitation. The environmental variable with the highest gain, when used in isolation, was the mean temperature of the wettest quarter. The environmental variable decreases the gain the most when it is, which therefore appears to have the most information that was not present in the other variables. [ABSTRACT FROM AUTHOR]

Pellionia scabra Benth. (PSB), a traditional Chinese medicinal plant, is widely used as folk treatment for rheumatic diseases. Assessing the impact of climate change on habitat suitability is critical for PSB resource utilization and ecological conservation. In this study, the maximum entropy model (MaxEnt) and the Geographic Information System (ARCGIS) were applied to predict the distribution of PSB, and the contribution of variables was evaluated using Jackknife test. The area under the receiver operating characteristic curve (AUC) of the PSB geographic distribution model reached a very good standard, indicating that the model prediction results can be used in this study. The potential distribution of PSB was mainly in Guangxi, Guangdong, Fujian, Hunan, Jiangxi, Zhejiang, Taiwan, Chongqing, Guizhou, southeast of Sichuan, southwest of Hubei, and south of Anhui (adaptability index > 0.5). The comprehensive analysis of the contribution rate of climatic factors and jackknife experiment showed that the precipitation of driest quarter (35.4%), precipitation of driest month (25.0%), annual precipitation (11.4%), mean diurnal range (7.9%) and precipitation of wettest quarter (3.9%) were the most important factors affecting the potential distribution of PSB. [ABSTRACT FROM AUTHOR]

Mus macedonicus Petrov & Ružic, 1983, also known as the Macedonian mouse or Balkan short-tailed mouse, lives in the southern Balkans and the Middle East. While this species is common in Mediterranean ecosystems and is listed as "least concern" by the IUCN, little is known about how its distribution may shift with climate change. This study explores the 'species' potential distribution in three different periods: during the Last Glacial Maximum (LGM) and under current and future climate scenarios, using Maximum Entropy modelling. Modelling was based on 137 georeferenced occurrence records from Macedonia, Bulgaria, Greece, Turkey, Syria, Lebanon, Israel, Iran, Georgia, and Russia and ten bioclimatic variables from the WorldClim database. We show that a combination of precipitation and temperature variables appear to shape the geographical range of the Macedonian mouse and that its predicted distribution during the LGM is consistent with its survival in multiple refugia, as suggested by previous genetic studies. Modelled future distributions are subtly but significantly different from the current, with population losses and gains in different regions. Our results provide a sound framework for future studies on this model species' range dynamics, suggesting that the overall geographical range of M. macedonicus is relatively stable in the long term. [ABSTRACT FROM AUTHOR]

Habitat assessment of species is one of the most important strategies to conserve biodiversity in the protected areas. The main objective of this study is to present an ecological assessment model for habitat management of brown bears using the MaxEnt algorithm in Oshtorankooh protected area, Lorestan. 55 presence points of brown bear and seven environmental variables including slope, elevation, distance from river, distance from road, distance from forest and grassland, distance from cropland and vegetation, and distance from rural area were applied for habitat assessment process. The importance of these variables was investigated by the Jaknikfe test and their predictive rate was assessed by response curves. The distance from the rural area and elevation were respectively the most important factors for modeling the distribution of brown bears in Oshtorankooh protected area. The final suitability map of habitat for brown bear species was classified into four categories: more suitable, suitable, less suitable and unsuitable. An area of 22566.7 ha was determined as a more suitable habitat for brown bears in the study area. The result indicates that the southern and central areas of the study area are more suitable for the species. The result of the model validity was obtained as 0.92, showing that the integrated model was very efficient in the habitat assessment process.

Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC, and Acc were 99.35%, 99.02%, 0.98, and 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC, and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.

Bangladesh is one of the world's most climate-vulnerable countries. The appraisal of household vulnerability and capacity to adapt under climate change is therefore crucial for developing effective disaster risk reduction policies. This study explored the nexus between vulnerability and capacity of rural drought-prone households using survey data of 400 respondents from northern Bangladesh. Through extensive rigorous literature review, 22 and 25 indicators were employed to develop the vulnerability and capacity index, respectively. Co-occurrence network analysis was employed to show the nexus among different classes of capacity and vulnerability. Spearman correlation was also performed to analyze the nexus between vulnerability and capacity. The results reveal that households' vulnerability varies across livelihood assets (human, social, financial, physical, and natural), but capacity follows a similar distribution pattern. A significant negative association was found between vulnerability and adaptive capacity, whereas the strong correlations between social and human capital, human and financial capital, social and financial capital, and physical and natural capital were identified in this study. Future projection (up to 2024) using support vector machine algorithm shows that overall drought adaptive capacity will increase, while vulnerability will decrease. The outcomes of this study would help planners and policy-makers in planning and taking proper initiatives that might be useful for adopting efficient disaster risk reduction strategies. [ABSTRACT FROM AUTHOR]

Ecological factors that control the species distribution patterns at various spatiotemporal scales will get affected by climate change. To combat the situation, in the past few decades geographical information system (GIS) and remote sensing have been widely used by the researchers in the field of wildlife and habitat suitability modeling. The main objective of this study is to map and predict the current and future habitat suitability potential of Rucervus eldii eldii in Keibul Lamjao National Park (KLNP) using MaxEnt. Presence location data of the species, topographic factors, and bio-climatic variables were used as input in the MaxEnt software to map current habitat suitability potential. To map the habitat suitability potential for future, two representative concentration pathway (RCP) scenarios RCP 2.6 and RCP 8.5 for the years 2050 and 2070 were used. The model returned an average AUC value of 0.944 which indicates the model to be sensitive and descriptive. Isothermality and precipitation in the wettest quarter were found to be two most significant variables. The suitable range of precipitation in the wettest quarter for Rucervus eldii eldii varies from 1365 to 1410 mm with an optimal value of 1405 mm and isothermality from 46.43 to 46.6% with an optimal value of 46.5%. Current habitat suitability results of the model show 0.45 km2 of the area under no potential, 29.25 km2 of the area under least potential, 8.29 km2 of the area under moderate potential, 9.21 km2 of the area under good potential, and 8.82 km2 of the area under high potential. Both RCPs for the years 2050 and 2070 show the decreasing trend in the area under high suitability potential and increasing trend under no suitability potential. The results of this study can provide aid in the management and protection of Rucervus eldii eldii. [ABSTRACT FROM AUTHOR]

Apoptosis proteins are related to many diseases. Obtaining the subcellular localization information of apoptosis proteins is helpful to understand the mechanism of diseases and to develop new drugs. At present, the researchers mainly focus on the primary protein sequences, so there is still room for improvement in the prediction accuracy of the subcellular localization of apoptosis proteins. In this paper, a new method named ERT-ECT-PSSM-IS is proposed to predict apoptosis proteins based on the position-specific scoring matrix (PSSM). First, the local and global features of different directions are extracted by evolutionary row transformation (ERT) and cross-covariance of evolutionary column transformation (ECT) based on PSSM (ERT-ECT-PSSM). Second, an improved isometric mapping algorithm (I-SMA) is used to eliminate redundant features. Finally, we adopt a support vector machine (SVM) to classify our results, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results show that the proposed method not only extracts more abundant feature expression but also has better predictive performance and robustness for the subcellular localization of apoptosis proteins in ZD98, ZW225, and CL317 databases. Graphical abstract Framework of the proposed prediction model. [ABSTRACT FROM AUTHOR]

American ginseng (Panax quinquefolius L.) is a well-known Asian traditional herbal medicine with a large market demand. The plant is native to eastern North America, and its main producing areas worldwide are decreasing due to continuous cropping obstacles and environmental changes. Therefore, the identification of maximum similarities of new ecological distribution of P. quinquefolius , and prediction of its response to climate change in the future are necessary for plant introduction and cultivation. In this study, the areas with potential ecological suitability for P. quinquefolius were predicted using the geographic information system for global medicinal plants (GMPGIS) based on 476 occurrence points and 19 bioclimatic variables. The results indicate that the new ecologically suitable areas for P. quinquefolius are East Asia and the mid-eastern Europe, which are mainly distributed in China, Russia, Japan, Ukraine, Belarus, North Korean, South Korea, andRomania. Under global climate change scenarios, the suitable planting areas for P. quinquefolius would be increased by 9.16%–30.97%, and expandingnorth and west over the current ecologically suitable areas by 2070. The potential increased areas that are ecologically suitable include northern Canada, Eastern Europe, and the Lesser Khingan Mountains of China, and reduced regions are mainly in central China, the southern U.S., and southern Europe. Jackknife tests indicate that the precipitation of the warmest quarter was the important climatic factor controlling the distribution of P. quinquefolius. Our findings can be used as auseful guide for P. quinquefolius introduction and cultivation in ecologically suitable areas. [ABSTRACT FROM AUTHOR]

The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures.

Protein functions are closely related to their subcellular locations. At present, the prediction of protein subcellular locations is one of the most important problems in protein science. The evident defects of traditional methods make it urgent to design methods with high efficiency and low costs. To date, lots of computational methods have been proposed. However, this problem is far from being completely solved. Recently, some multi-label classifiers have been proposed to identify subcellular locations of human, animal, Gram-negative bacterial and eukaryotic proteins. These classifiers adopted the protein features derived from gene ontology information. Although they provided good performance, they can be further improved by adopting more powerful machine learning algorithms. In this study, four improved multi-label classifiers were set up for identification of subcellular locations of the above four protein types. The random k-labelsets (RAKEL) algorithm was used to tackle proteins with multiple locations, and random forest was used as the basic prediction engine. All classifiers were tested by jackknife test, indicating their high performance. Comparisons with previous classifiers further confirmed the superiority of the proposed classifiers.

Highlights • The amino acid compositions of three groups of ion channel inhibitors were analyzed. • The feature selection method was used to select the important features. • The random forest was used to predict three groups of ion channel inhibitors. Abstract Venomous animals produce toxins that inhibit ion channels with high affinity. These small peptide inhibitors are used in the characterization of ion channels structurally as well as pharmacologically. So, identification of these toxins is an important task. In this study, based on the pseudo amino acid (PseAA) composition and feature selection method, the random forest algorithm was used for predicting three different groups of ion channel inhibitors. The prediction results indicated that our algorithm achieved the sensitivity of 60.00% for calcium channel inhibitor, 71.90% for potassium channel inhibitor and 86.80% for sodium channel inhibitor when evaluated by the jackknife test. In addition, for comparing with other algorithms, this algorithm was used to predict the dataset with 343 ion channel inhibitors, and the higher predictive success rates than the previous algorithms were obtained by our algorithm. [ABSTRACT FROM AUTHOR]

Presynaptic neurotoxins and postsynaptic neurotoxins are two important neurotoxins isolated from venoms of venomous animals and have been proven to be potential effective in neurosciences and pharmacology. With the number of toxin sequences appeared in the public databases, there was a need for developing a computational method for fast and accurate identification and classification of the novel presynaptic neurotoxins and postsynaptic neurotoxins in the large databases. In this study, the Multinomial Naive Bayes Classifier (MNBC) had been developed to discriminate the presynaptic neurotoxins and postsynaptic neurotoxins based on the different kinds of features. The Minimum Redundancy Maximum Relevance (MRMR) feature selection method was used for ranking 400 pseudo amino acid (PseAA) compositions and 50 top ranked PseAA compositions were selected for improving the prediction results. The motif features, 400 PseAA compositions and 50 PseAA compositions were combined together, and selected as the input parameters of MNBC. The best correlation coefficient (CC) value of 0.8213 was obtained when the prediction quality was evaluated by the jackknife test. It was anticipated that the algorithm presented in this study may become a useful tool for identification of presynaptic neurotoxin and postsynaptic neurotoxin sequences and may provide some useful help for in-depth investigation into the biological mechanism of presynaptic neurotoxins and postsynaptic neurotoxins. [ABSTRACT FROM AUTHOR]

Between December 2013 and January 2016, a major outbreak of Peste des petits ruminants (PPR) has been registered in Mainland China. 39184 cases have been reported from 22 provinces. Animal losses and economic damages were alarming and it cannot yet be ruled out that the virus may spread to as of yet unaffected areas. Using data collected during this outbreak, we used maximum entropy model to identify risk factors potentially influence the prevalence of PPR. In cooperation with Department of Veterinary Services of PR China, who provided all essential data, we were able to evaluate the collinearity of risk factors and their influence on the disease transmission. Spearman rank correlation analysis and jackknife test were the main statistical tests we applied. Our findings indicate that the disease prevalence significantly correlates with mean monthly temperature, relative humidity, age, male/female sex, the number of live animal markets and the quantum of trade. Based on the final results of our risk factor analysis, we continued to realize a geographical analysis. From the finally prediction map, we find that provinces of Xinjiang, Anhui, Jiangsu, Zhejiang, Yunnan and Guizhou have high risk of disease outbreaks. We expected that the results of our study can be utilized to develop solutions for targeted disease prevention and aid management. [ABSTRACT FROM AUTHOR]

The Mw 4.3 earthquake of September 2015 is the first felt earthquake since 1900 A.D in the northern part of the Western Desert, Egypt, south of the El-Alamein City. The available waveform data observed at epicentral distances 52–391 km was collected and carefully evaluated. Nine broad-band stations were selected to invert full waveforms for the centroid position (horizontal and vertical) and for the focal mechanism solution. The first-arrival travel times, polarities and low-frequency full waveforms (0.03–0.08 Hz) are consistently explained in this paper as caused by a shallow source of the strike-slip mechanism. This finding indicates causal relation of this earthquake to the W-E trending South El-Alamein fault, which developed in Late Cretaceous as dextral strike slip fault. Recent activity of this fault, proven by the studied rare earthquake, is of fundamental importance for future seismic hazard evaluations, underlined by proximity (∼65 km) of the source zone to the first nuclear power plant planned site in Egypt. Safe exploration and possible future exploitation of hydrocarbon reserves, reported around El-Alamein fault in the last decade, cannot be made without considering the seismic potential of this fault. [ABSTRACT FROM AUTHOR]

The potential distribution of Olea ferruginea was predicted by Maxent model for present and the upcoming hypothetical (2050) climatic scenario. O. ferruginea is an economically beneficial plant species. For predicting the potential distribution of O. ferruginea in Pakistan, Worldclim variables for current and future climatic change scenarios, digital elevation model (DEM) slope, and aspects with the occurrence point were used. Pearson correlation was used to reject highly correlated variables. A total of 219 sighting points were used in the Maxent modeling. The area under curve (AUC) value was higher than 0.98. The approach used in this study is considered useful in predicting the potential distribution of O. ferruginea species, and can be an effective tool in the conservation and restoration planning for human welfare. The results show that there is a significant impact under future bioclimatic scenarios on the potential distribution of O. ferruginea in Pakistan. There is a significant decrease in the overall distribution of O. ferruginea due to loss of habitats under current distribution range, but this will be compensated by gain of habitat at higher altitudes in the future climate change scenario (habitat shift). It is recommended that the areas predicted suitable for the O. ferruginea may be used for plantation of this species while the deforested land should be restored for human welfare. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) play an important role in the innate immune system that evolved in most living organisms. As a kind of natural antibiotics, it is promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop a fast and effective computational method for accurately predicting the functional types of AMPs, because the biological functions of AMPs are correlated with the type it belongs to. Although many efforts have been made in this area, to the best of our knowledge, most of the existing predictors only has the ability to deal with whether a peptide is an AMP or not, or a peptide belongs to which one type. However, there are many AMPs have two or more functional types, the phenomenon should worthy of our special notice, because they may have some unique biological functions for new drug design and disease treatment. In this study, in order to reflect the characteristic of multiplex AMPs, a new multi-label classifier based on sequence information and multi-label learning with label-specific features (LIFT) algorithm was developed. It was observed that, the absolute-true with jackknife test by the new predictor on a newly stringent benchmark dataset is 0.5040, and the success rates achieved by the new predictor are 5 % higher than this by iAMP-2L in the same dataset, indicating that our method is quite promising. We hope that the predictor may become a useful high-through tool in identifying the functional types of AMPs. [ABSTRACT FROM AUTHOR]

Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their type. Given a membrane protein sequence, how can we identify its type(s)? Particularly, how can we deal with the multi-type problem since one membrane protein may simultaneously belong to two or more different types? To address these problems, which are obviously very important to both basic research and drug development, a new multi-label classifier was developed based on pseudo amino acid composition with multi-label k-nearest neighbor algorithm. The success rate achieved by the new predictor on the benchmark dataset by jackknife test is 73.94 %, indicating that the method is promising and the predictor may become a very useful high-throughput tool, or at least play a complementary role to the existing predictors in identifying functional types of membrane proteins. [ABSTRACT FROM AUTHOR]