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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]

Membrane protein is an important composition of cell membrane. Given a membrane protein sequence, how can we identify its type(s) is very important because the type keeps a close correlation with its functions. According to previous studies, membrane protein can be divided into the following eight types: single-pass type I, single-pass type II, single-pass type III, single-pass type IV, multipass, lipid-anchor, GPI-anchor, peripheral membrane protein. With the avalanche of newly found protein sequences in the post-genomic age, it is urgent to develop an automatic and effective computational method to rapid and reliable prediction of the types of membrane proteins. At present, most of the existing methods were based on the assumption that one membrane protein only belongs to one type. Actually, a membrane protein may simultaneously exist at two or more different functional types. In this study, a new method by hybridizing the pseudo amino acid composition with multi-label algorithm called LIFT (multi-label learning with label-specific features) was proposed to predict the functional types both singleplex and multiplex animal membrane proteins. Experimental result on a stringent benchmark dataset of membrane proteins by jackknife test show that the absolute-true obtained was 0.6342, indicating that our approach is quite promising. It may become a useful high-through tool, or at least play a complementary role to the existing predictors in identifying functional types of membrane proteins. [ABSTRACT FROM AUTHOR]

Abstract: Drug-target interaction is a key research topic in drug discovery since correct identification of target proteins of drug candidates can help screen out those with unacceptable toxicities, thereby saving expense. In this study, we developed a novel computational approach to predict drug target groups that may reduce the number of candidate target proteins associated with a query drug. A benchmark dataset, consisting of 3028 drugs assigned within nine categories, was constructed by collecting data from KEGG. The nine categories are (1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens. The proposed method combines the data gleaned from chemical–chemical similarities, chemical–chemical connections and chemical–protein connections to allocate drugs to each of the nine target groups. A jackknife test applied to the training dataset that was constructed from the benchmark dataset, provided an overall correct prediction rate of 87.45%, as compared to 87.79% for the test dataset that was constructed by randomly selecting 10% of samples from the benchmark dataset. These prediction rates are much higher than the 11.11% achieved by random guesswork. These promising results suggest that the proposed method can become a useful tool in identifying drug target groups. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai. [Copyright &y& Elsevier]

Apoptosis proteins play an essential role in the development and homeostasis of an organism. The accurate prediction of subcellular location for apoptosis proteins is helpful for understanding the mechanism of programmed cell death and their biological functions. In this article, a new apoptosis proteins localization algorithm, named PSSP, is proposed based on the predicted cleavage sites of primary protein sequences. First, protein chains are divided into N-terminal signal parts and mature protein parts according to their predicted cleavage sites by SignalP. Then, amino acid composition (ACC) of the individual subsequence together with pseudo-ACC and stereochemical properties of whole chain were extracted to represent a given protein sequence. Jackknife test by support vector machine on three broadly used datasets (ZD98, ZW225, and CL317 datasets) of apoptosis proteins demonstrated that the total accuracies by this approach are 93.9, 87.6, and 91.5%, respectively. In addition, an independent nonapoptosis benchmark dataset (NNPSL) was also used to evaluate the performance of this method, and predictive accuracies for eukaryotic and prokaryotic proteins are also comparable to existing methods. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Knowing the subcellular localization of proteins within the cell is an important step in elucidating its role in biological processes, its function and its potential as a drug target for disease diagnosis. As the number of complete genomes rapidly increases, accurate and efficient methods that automatically predict the subcellular localizations become more urgent. In the current paper, we developed a novel method that coupled the discrete wavelet transform with support vector machine based on the amino acid polarity to predict the subcellular localizations of prokaryotic and eukaryotic proteins. The results obtained by the jackknife test were quite promising, and indicated that the proposed method remarkably improved the prediction accuracy of subcellular locations, and could be as an effective and promising high-throughput method in the subcellular localization research. [Copyright &y& Elsevier]

Abstract: In vivo, some proteins exist as monomers (single polypeptide chains) and others as oligomers. Not like monomers, oligomers are composed of two or more chains (subunits) that are associated with each other through non-covalent interactions and, occasionally, through disulfide bonds. These proteins are the structural components of various biological functions, including cooperative effects, allosteric mechanisms and ion-channel gating. However, with the dramatic increase in the number of protein sequences submitted to the public data bank, it is important for both basic research and drug discovery research to acquire the possible knowledge about homo-oligomeric attributes of their interested proteins in a timely manner. In this paper, a high-throughput method, combined support vector machines with discrete wavelet transform, has been developed to predict the protein homo-oligomers. The total accuracy obtained by the re-substitution test, jackknife test and independent dataset test are 99.94%, 96.17% and 96.18%, respectively, showing that the proposed method of extracting feature from the protein sequences is effective and feasible for predicting homo-oligomers. The online service is available at http://bioinfo.ncu.edu.cn/Services.aspx. [Copyright &y& Elsevier]

Abstract: Many proteins exist in vivo as oligomers with different quaternary structural attributes rather than as individual chains. These proteins are the structural components of various biological functions, including cooperative effects, allosteric mechanisms and ion-channel gating. With the dramatic increase in the number of protein sequences submitted to the public databank, it is important for both basic research and drug discovery research to acquire the knowledge about possible quaternary structural attributes of their interested proteins in a timely manner. A high-throughput method (DWT_SVM), fusing discrete wavelet transform (DWT) and support vector machine (SVM) classifier algorithm with various physicochemical features, has been developed to predict protein quaternary structure. The accuracy in distinguishing candidate proteins as homo-oligomer or hetero-oligomer using the dataset R2720 was 85.95% and 85.49% respectively by jackknife, showing that DWT_SVM is guide promising in predicting protein quaternary structures. The online service is available at http://bioinfo.ncu.edu.cn/Services.aspx. Protein sequences in FASTA format can be directly fed to the system OligoPred. The processed results will be presented in a diagram that includes the information of feature extraction and the classification error rate. [Copyright &y& Elsevier]

Abstract: In this paper, we intend to predict protein structural classes (, , , or ) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher''s linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes. [Copyright &y& Elsevier]

Abstract: Optimally weighted fuzzy k-nearest neighbors (OWFKNN) algorithm has been used to predict proteins’ subcellular locations based on their amino acid composition, in this paper. The datasets used consists of two species which are 997 prokaryotic and 2427 eukaryotic protein sequences. The overall prediction accuracy achieved is about 88.5% for prokaryotic sequences and 86.2% for eukaryotic sequences in a jackknife test. Compared to other algorithms developed for the prediction of protein subcellular location, OWFKNN gives very satisfying results. Therefore, OWFKNN can be used as an alternative method to predict protein localization. [Copyright &y& Elsevier]

Abstract: The nucleus is the brain of eukaryotic cells that guides the life processes of the cell by issuing key instructions. For in-depth understanding of the biochemical process of the nucleus, the knowledge of localization of nuclear proteins is very important. With the avalanche of protein sequences generated in the post-genomic era, it is highly desired to develop an automated method for fast annotating the subnuclear locations for numerous newly found nuclear protein sequences so as to be able to timely utilize them for basic research and drug discovery. In view of this, a novel approach is developed for predicting the protein subnuclear location. It is featured by introducing a powerful classifier, the optimized evidence-theoretic K-nearest classifier, and using the pseudo amino acid composition [K.C. Chou, PROTEINS: Structure, Function, and Genetics, 43 (2001) 246], which can incorporate a considerable amount of sequence-order effects, to represent protein samples. As a demonstration, identifications were performed for 370 nuclear proteins among the following 9 subnuclear locations: (1) Cajal body, (2) chromatin, (3) heterochromatin, (4) nuclear diffuse, (5) nuclear pore, (6) nuclear speckle, (7) nucleolus, (8) PcG body, and (9) PML body. The overall success rates thus obtained by both the re-substitution test and jackknife cross-validation test are significantly higher than those by existing classifiers on the same working dataset. It is anticipated that the powerful approach may also become a useful high throughput vehicle to bridge the huge gap occurring in the post-genomic era between the number of gene sequences in databases and the number of gene products that have been functionally characterized. The OET-KNN classifier will be available at www.pami.sjtu.edu.cn/people/hbshen. [Copyright &y& Elsevier]

Abstract: The subcellular location of a protein is closely correlated with it biological function. In this paper, two new pattern classification methods termed as Nearest Feature Line (NFL) and Tunable Nearest Neighbor (TNN) have been introduced to predict the subcellular location of proteins based on their amino acid composition alone. The simulation experiments were performed with the jackknife test on a previously constructed data set, which consists of 2427 eukaryotic and 997 prokaryotic proteins. All protein sequences in the data set fall into four eukaryotic subcellular locations and three prokaryotic subcellular locations. The NFL classifier reached the total prediction accuracies of 82.5% for the eukaryotic proteins and 91.0% for the prokaryotic proteins. The TNN classifier reached the total prediction accuracies of 83.6 and 92.2%, respectively. It is clear that high prediction accuracies have been achieved. Compared with Support Vector Machine (SVM) and Nearest Neighbor methods, these two methods display similar or even higher prediction accuracies. Hence, we conclude that NFL and TNN can be used as complementary methods for prediction of protein subcellular locations. [Copyright &y& Elsevier]

Abstract: To understand the structure and function of a protein, an important task is to know where it occurs in the cell. Thus, a computational method for properly predicting the subcellular location of proteins would be significant in interpreting the original data produced by the large-scale genome sequencing projects. The present work tries to explore an effective method for extracting features from protein primary sequence and find a novel measurement of similarity among proteins for classifying a protein to its proper subcellular location. We considered four locations in eukaryotic cells and three locations in prokaryotic cells, which have been investigated by several groups in the past. A combined feature of primary sequence defined as a 430D (dimensional) vector was utilized to represent a protein, including 20 amino acid compositions, 400 dipeptide compositions and 10 physicochemical properties. To evaluate the prediction performance of this encoding scheme, a jackknife test based on nearest neighbor algorithm was employed. The prediction accuracies for cytoplasmic, extracellular, mitochondrial, and nuclear proteins in the former dataset were 86.3%, 89.2%, 73.5% and 89.4%, respectively, and the total prediction accuracy reached 86.3%. As for the prediction accuracies of cytoplasmic, extracellular, and periplasmic proteins in the latter dataset, the prediction accuracies were 97.4%, 86.0%, and 79.7, respectively, and the total prediction accuracy of 92.5% was achieved. The results indicate that this method outperforms some existing approaches based on amino acid composition or amino acid composition and dipeptide composition. [Copyright &y& Elsevier]

The support vector machine approach was introduced to predict the β-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the β-turn subset of 455 tetrapeptides and non-β-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the β-turn subset of 110 tetrapeptides and non-β-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a β-turn. [Copyright &y& Elsevier]

In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino acid composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins. [Copyright &y& Elsevier]

Support Vector Machines (SVMs) which is one kind of learning machines, was applied to predict the specificity of GalNAc-transferase. The examination for the self-consistency and the jackknife test of the SVMs method were tested for the training dataset (305 oligopeptides), the correct rate of self-consistency and jackknife test reaches 100% and 84.9%, respectively. Furthermore, the prediction of the independent testing dataset (30 oligopeptides) was tested, the rate reaches 76.67%. [Copyright &y& Elsevier]

N 6 , TL-Methy method reached the accuracy of 91.68% on jackknife test and of 92.23% on 10-fold cross validation test; For Homo sapiens , TL-Methy method achieved the accuracy of 93.66% on jackknife test and of 97.07% on 10-fold cross validation test; For Mus musculus , TL-Methy method obtained the accuracy of 81.57% on jackknife test and of 82.54% on 10-fold cross validation test; For Saccharomyces cerevisiae , TL-Methy method achieved the accuracy of 91.87% on jackknife test and of 93.04% on 10-fold cross validation test. The results via these two test approaches demonstrated the robustness and practicality of our TL-Methy model. The TL-Methy model may be as a potential method for m6A site identification.Communicated by Ramaswamy H. Sarma.rice genome , TL-Methy method achieved the accuracy of 91.87% on jackknife test and of 93.04% on 10-fold cross validation test. The results via these two test approaches demonstrated the robustness and practicality of our TL-Methy model. The TL-Methy model may be as a potential method for m6A site identification.Communicated by Ramaswamy H. Sarma.

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. [ABSTRACT FROM AUTHOR]

This study presents the tree species distribution and habitat suitability maps in Gilgit-Baltistan, Pakistan at 1 km spatial resolution. This study is based on bioclimatic and topographical variables and 440 samples of six native trees species: Abies pindrow , Betula utilis, Cedrus deodara, Picea smithiana, Pinus wallichiana, and Quercus ilex. Data is collected through field survey. Exclusively for each tree species, a multicollinearity test was performed among 24 independent or environment variables (21 bioclimatic and 3 topographic). The highly correlated independent variables (r ≥ 0.9, Pearson correlation coefficient) were eliminated from the independent variables list. In this study, we employed the Maximum Entropy (MaxEnt) model to produce current (2015–2016) as well as RCP4.5 and RCP8.5 climate-change scenarios by 2050 for tree species spatial distribution results. The jackknife test was carried out to depict the importance of variables with the highest gain and it was observed that overall elevation, precipitation, and temperature are the factors with the highest gain. The results of the MaxEnt model for each tree species were satisfactory with ROC (receiver operating characteristic) AUC (area under the curve) curve training and testing values greater than 0.9 and 0.84 respectively. Based on 10-percentile training presence threshold-dependent values, the overall accuracy of True Skill Statistics (TSS) was more than 80%. The maximum area coverage of all tree species existed under "inadmissible natural surroundings (0–0.2 probability)" and least area fell under "exceptionally appropriate environment (0.6–0.7 probability)" to "profoundly reasonable living space (0.7–1.0 probability)". A tree species diversity map prepared through equal weighted average overlay analysis, using all six developed tree species probability outputs. The field observation might possess certain limitations because it was difficult for the field crew to access the areas with rough terrain, long distances, harsh weather conditions, and locations of forest in steep, narrow valleys. Overall, this study contributes to enlarge tree species distribution research datasets applicability in Pakistan and over the Hindu Kush Himalayan (HKH) mountains region. It may also provide interesting insight, which could be used for the habitat corridor suitability modelling of endangered species, and ground intervention to protect and expand tree species distributions. [ABSTRACT FROM AUTHOR]

Abstract: Cell membranes are vitally important to living cells. Although the infrastructure of biological membrane is provided by the lipid bilayer, membrane proteins perform most of the specific functions. Knowledge of membrane protein types often provides crucial hints toward determining the function of an uncharacterized membrane protein. With the avalanche of new protein sequences generated in the post-genomic era, it is highly demanded to develop a high throughput tool in identifying the type of newly found membrane proteins according to their primary sequences, so as to timely annotate them for reference usage in both basic research and drug discovery. To realize this, the key is to establish a powerful identifier that can catch their characteristic sequence patterns for different membrane protein types. However, it is not easy because they are buried in a pile of long and complicated sequences. In this paper, based on the concept of the pseudo-amino acid composition [K.C. Chou, PROTEINS: Struct., Funct., Genet. 43 (2001) 246–255], the low-frequency Fourier spectrum analysis is introduced. The merits by doing so are that the sequence pattern information can be more effectively incorporated into a set of discrete components, and that all the existing prediction algorithms can be straightforwardly used on such a formulation for protein samples. High success rates were observed by the re-substitution test, jackknife test, and independent dataset test, indicating that the low-frequency Fourier spectrum approach may become a very useful tool for membrane protein type prediction. The novel approach also holds a high potential for predicting many other attributes of proteins. [Copyright &y& Elsevier]

Given a nascent protein sequence, how can one predict its signal peptide or “Zipcode” sequence? This is an important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy (see, e.g. K.C. Chou, Current Protein and Peptide Science 2002;3:615–22). In this paper, support vector machines (SVMs), a new machine learning method, is applied to approach this problem. The overall rate of correct prediction for 1939 secretary proteins and 1440 nonsecretary proteins was over 91%. It has not escaped our attention that the new method may also serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells. [Copyright &y& Elsevier]