In extremely cold environments, living organisms like plants, animals, fishes, and microbes can die due to the intracellular ice formation in their bodies. To sustain life in such cold environments, ...some cold-blooded species produced Antifreeze proteins (AFPs), also called ice-binding proteins. AFPs are not only limited to the medical field but also have diverse significance in the area of biotechnology, agriculture, and the food industry. Different AFPs exhibit high heterogeneity in their structures and sequences. Keeping the significance of AFPs, several machine-learning-based models have been developed by scientists for the prediction of AFPs. However, due to the complex and diverse nature of AFPs, the prediction performance of the existing methods is limited. Therefore, it is highly indispensable for researchers to develop a reliable computational model that can accurately predict AFPs. In this connection, this study presents a novel predictor for AFPs, named AFP-CMBPred. The sequences of AFPs are formulated via four different feature representation methods, such as Amphiphilic pseudo amino acid composition (Amp-PseAAC), Dipeptide Deviation from Expected Mean (DDE), Multi-Blocks Position Specific Scoring Matrix (MB-PSSM), and Consensus Sequence-based on Multi-Blocks Position Specific Scoring Matrix (CS-MB-PSSM) to collect local and global descriptors. In the next step, the extracted feature vectors are evaluated via Support Vector Machine (SVM) and Random Forest (RF) based classification learners. The prediction performance of both classifiers is further assessed using three validation methods i.e., jackknife test, 10-fold cross-validation test, and independent test. After examining the prediction rates of all validation tests, it was found that our proposed model achieved the higher prediction accuracies of ∼2.65%, ∼2.84%, and ∼3.37% using jackknife, K-fold, and independent test, respectively. The experimental outcomes validate that our proposed “AFP-CMBPred” predictor secured the highest prediction results than the existing models for the identification of AFPs. It is further anticipated that our proposed AFP-CMBPred model will be considered a valuable tool in the research academia and drug development.
•Designed a novel predictor named AFP-CMBPred for prediction of Antifreeze proteins.•The local and global discriminative features are explored by Amp-PseAAC, DDE, MB-PSSM, and CS-MB-PSSM.•SVM and RF are used as classification algorithms.•AFP-CMBPred predictor secured the highest prediction results for AFPs identification.
Khapra beetle, Trogoderma granarium is considered as a major threat to stored wheat and other products in all over the world. Their attack is not only limited to wheat grains but also reduces the ...various products made from it. The losses are defined as a measurable decrease of the food quantity and quality and can be avoided through proper control measures and selection of resistant varieties. During the present study, the different wheat varieties were screened against T. granarium. The initial weight of healthy grains of each variety was recorded properly. The losses caused by pests were categorized on the basis of grain weight loss (%), powder weight (mg) and population of pests emerged. In results, it was observed that the attack of T. granarium was present more or less on all wheat varieties. However, the performance of Marvi and Mehran varieties was found to be resistant against T. granarium and thus recommended for future plantations. The present study will be useful to provide the information for controlling T. granarium on wheat.
In engineering and technology safety of human life has always been a top priority. With the increasing usage of vehicles in everyday life, probability of deaths and injuries has also increased. This ...paper provides a critical review on the optimization of vehicle frontal crashworthiness studied by researchers using various methods. They investigated the effects of crash at a defined speed using the method of FRB and ODB impact. It further discusses other methods that can be used to save passengers’ life. Also, the designing and manufacturing limitations faced by engineers in actual development processes. Finally, it is concluded that improved structure design and material composition can significantly increase the overall crashworthiness of the vehicle.
Lectins are types of glycoprotein that have a wide variety of different species which play an important part in tumor discrimination due to their meaningful binding resemblance to different types of ...saccharide (carbohydrate) groups of the protein. Cancerlectins are those lectins that are firmly identified with specific kinds of proteins, which begin cancer cell endurance, development, metastasis, and spread of cancer. Differentiation of a protein based on its functionality remains a difficult job in the post-genomic era. The study of protein-specific function differentiation plays important role in therapeutic cancer studies. Lab-based methods were presented for prediction of cancerlectins. However, these approaches are expensive and time-consuming. Numerous computational sequence-based approaches have been developed to separate cancerlectins from non-cancerlectins. In our proposed study, we have designed a fast deep learning model for the discrimination of cancerlectins from non-cancerlectins on sequence-based feature descriptive techniques. The proposed model discovered intrinsic features by Conjoint Trade (CT), Pseudo Amino Acid Composition (PseAAC), and Position Specific Scoring Matrix (PSSM). The feature vector of these descriptors was concatenated and selected the best features by Random Forest-Sequential Feature Selection (RF-SFS). The model training and prediction were performed with Decision Tree (DT), Random Forest Classifier (RFC), Support Vector Machine (SVM), and Deep Neural Network (DNN).
The DNN showed the best performance and secured 89.40% accuracy, 80.84% sensitivity, and 94.62% specificity. These experimental results show the sturdiness of the proposed study and surpassed all the current methodology in the literature. We believe that the proposed strategy will be a helpful instrument in the malignant growth therapeutics research, drug plan, and scholarly examination considers.
•Designed a novel predictor named Deep-PCL for prediction of cancer-lectins.•The features are explored by Conjoint Trade, Pseudo Amino Acid Composition, and Position Specific Scoring Matrix.•SVM, DT, DNN, and RFC are used for models training and classification.•Deep-PCL predictor secured the highest prediction results for cancer-lectins identification.
This paper examines the manner in which audits would be conducted in the future and how technology has transformed and impacted the business processes of public, private sector entities and various ...organisations and the guidelines which need to be followed to ensure compliance with applicable laws and regulations. The tranperancy of financial statements is of paramount interest to shareholders and other significant stakeholders. This necessities that the financial statements are audited to acquire a certain level of confidence over the integrity of numbers and the validity of business rationale which thereby arises a need for auditor to be well equipped with all the tools and system essentials in carrying out an effective and efficient audit. Information Technology can act as an impediment or stimulant towards the achievement of the above discussed objective. Various organizations use automation tools and ERP applications which have become a vital cog in their internel control environment. Understanding by auditor of these automated controls is necessary to ensure that the they are well equipped with the requisite skills and have knowledge of all technological tweaks that would be required in the audit process of a complex structured entity. The primary function which can be performed by generalized audit software include customizing data in numrous ways to serve the distinct purpose. The audit teams obtain insights into latest developments and plan their procedures accordingly keeping in view the applicable professional standards.
A recent line of research has focused on Ubiquitination, a pervasive and proteasome-mediated protein degradation that controls apoptosis and is crucial in the breakdown of proteins and the ...development of cell disorders, is a major factor. The turnover of proteins and ubiquitination are two related processes. We predict ubiquitination sites; these attributes are lastly fed into the extreme gradient boosting (XGBoost) classifier. We develop reliable predictors computational tool using experimental identification of protein ubiquitination sites is typically labor- and time-intensive. First, we encoded protein sequence features into matrix data using Dipeptide Deviation from Expected Mean (DDE) features encoding techniques. We also proposed 2nd features extraction model named dipeptide composition (DPC) model. It is vital to develop reliable predictors since experimental identification of protein ubiquitination sites is typically labor- and time-intensive. In this paper, we proposed computational method as named Ubipro-XGBoost, a multi-view feature-based technique for predicting ubiquitination sites. Recent developments in proteomic technology have sparked renewed interest in the identification of ubiquitination sites in a number of human disorders, which have been studied experimentally and clinically. When more experimentally verified ubiquitination sites appear, we developed a predictive algorithm that can locate lysine ubiquitination sites in large-scale proteome data. This paper introduces Ubipro-XGBoost, a machine learning method. Ubipro-XGBoost had an AUC (area under the Receiver Operating Characteristic curve) of 0.914% accuracy, 0.836% Sensitivity, 0.992% Specificity, and 0.839% MCC on a 5-fold cross validation based on DPC model, and 2nd 0.909% accuracy, 0.839% Sensitivity, 0.979% Specificity, and 0. 0.829% MCC on a 5-fold cross validation based on DDE model. The findings demonstrate that the suggested technique, Ubipro-XGBoost, outperforms conventional ubiquitination prediction methods and offers fresh advice for ubiquitination site identification.
The majority of cytoplasmic proteins and vesicles move actively primarily to dynein motor proteins, which are the cause of muscle contraction. Moreover, identifying how dynein are used in cells will ...rely on structural knowledge. Cytoskeletal motor proteins have different molecular roles and structures, and they belong to three superfamilies of dynamin, actin and myosin. Loss of function of specific molecular motor proteins can be attributed to a number of human diseases, such as Charcot-Charcot-Dystrophy and kidney disease. It is crucial to create a precise model to identify dynein motor proteins in order to aid scientists in understanding their molecular role and designing therapeutic targets based on their influence on human disease. Therefore, we develop an accurate and efficient computational methodology is highly desired, especially when using cutting-edge machine learning methods. In this article, we proposed a machine learning-based superfamily of cytoskeletal motor protein locations prediction method called extreme gradient boosting (XGBoost). We get the initial feature set All by extraction the protein features from the sequence and evolutionary data of the amino acid residues named BLOUSM62. Through our successful eXtreme gradient boosting (XGBoost), accuracy score 0.8676%, Precision score 0.8768%, Sensitivity score 0.760%, Specificity score 0.9752% and MCC score 0.7536%. Our method has demonstrated substantial improvements in the performance of many of the evaluation parameters compared to other state-of-the-art methods. This study offers an effective model for the classification of dynein proteins and lays a foundation for further research to improve the efficiency of protein functional classification.
There are group of challenges in VoIP security, VoIP Quality and lots of peoples worked on it e.g., “IPSec security on VoIP”, “VoIP Honeypot architecture”, “cryptography techniques are used to safely ...transmit the information stream over the network” and many more. Security is a terminology which cannot be a perfect or 100%. For the time being we can be minimizing and protect to the threats but as the technology increases the new threats are also generating day by day. Researchers have applied different patterns, techniques and scenarios to prevent some specific threats and security frameworks for securing VoIP communication. But in this research, we want to analyze the quality of service after applying Honeynet security framework.
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