The emergence of SARS-CoV-2 variants with mutations in the spike protein is raising concerns about the efficacy of infection- or vaccine-induced antibodies. We compared antibody binding and live ...virus neutralization of sera from naturally infected and Moderna-vaccinated individuals against two SARS-CoV-2 variants: B.1 containing the spike mutation D614G and the emerging B.1.351 variant containing additional spike mutations and deletions. Sera from acutely infected and convalescent COVID-19 patients exhibited a 3-fold reduction in binding antibody titers to the B.1.351 variant receptor-binding domain of the spike protein and a 3.5-fold reduction in neutralizing antibody titers against SARS-CoV-2 B.1.351 variant compared to the B.1 variant. Similar results were seen with sera from Moderna-vaccinated individuals. Despite reduced antibody titers against the B.1.351 variant, sera from infected and vaccinated individuals containing polyclonal antibodies to the spike protein could still neutralize SARS-CoV-2 B.1.351, suggesting that protective humoral immunity may be retained against this variant.
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•Antibodies from infected and vaccinated individuals bind to the B.1.351 RBD•Convalescent sera through eight months can neutralize the B.1.351 variant•Serum from vaccinated individuals retains neutralization against the B.1.351 variant
In this study, Edara et al. (2021) report that, despite reduced antibody binding to the B.1.351 RBD, sera from infected (acute and convalescent) and Moderna (mRNA-1273)-vaccinated individuals were still able to neutralize the SARS-CoV-2 B.1.351 variant, suggesting that protective humoral immunity may be retained against this variant.
The recent advancements in science, engineering, and technology have facilitated huge generation of datasets. These huge datasets contain noisy, redundant, and irrelevant features which negatively ...affects the performance of classification techniques in machine learning and data mining process. Feature selection is a pre-processing stage for reducing the dimensionality of datasets by selecting the most important attributes while increasing the accuracy of classification at the same time. In this paper, we present a novel hybrid binary version of enhanced chaotic crow search and particle swarm optimization algorithm (ECCSPSOA) to solve feature selection problems. In the proposed ECCSPSOA, in order to navigate the feature space, we hybridized the enhanced version of the CSA algorithm which has a better search strategy and particle swarm optimization (PSO) which is capable of converging into the best global solution in the search field. We further embed opposition-based learning technique in the local search of the hybrid algorithm. The ECCSPSOA was compared using 15 datasets from the UCI repository with four well-known optimization algorithms, such as particle swarm optimization (PSO), binary particle swarm optimization (BPSO), crow search algorithm (CSA), and chaotic crow search algorithm (CCSA). In the experiments with k-Nearest Neighbour (KNN) as a classifier, six different performance metrics were used. To tackle the over-fitting problem, each dataset is divided into training and testing data using K-fold cross-validation. The computational findings demonstrate that the proposed algorithm obtains an average accuracy rate of 89.67 % over 15 datasets, indicating that our technique exceeds state-of-the-art findings in 12 of the 15 datasets studied. Furthermore, the suggested approach outperforms state-of-the-art methods in terms of fitness value and standard deviation, obtaining the lowest value in 13 and 8 of the datasets studied respectively.
•Handcrafted features techniques for extracting texture, shape, and color features for efficient classification.•DNN classifier based on dense layer and softmax for efficient ...multi-classification.•Data augmentation method to address the problem of over fitting while improving classification accuracy is presented and segmentation on pre-processing stage.•Comparison of the proposed method and other works using histopathological images using BreakHis data.•The obtained results showcased the effectiveness of the proposed model for BC multi-classification.
Breast cancer (BC) classification has become a point of concern within the field of biomedical informatics in the health care sector in recent years. This is because it is the second-largest cause of cancer-related fatalities among women. The medical field has attracted the attention of researchers in applying machine learning techniques to the detection, and monitoring of life-threatening diseases such as breast cancer (BC). Proper detection and monitoring contribute immensely to the survival of BC patients, which is largely dependent on the analysis of pathological images. Automatic detection of BC based on pathological images and the use of a Computer-Aided Diagnosis (CAD) system allow doctors to make a more reliable decision. Recently, Deep Learning algorithms like Convolution Neural Network have been proven to be reliable in detecting BC targets from pathological images. Several research efforts have been undertaken in the binary classification of histopathological images. However, few approaches have been proposed for the multi-classification of histopathological images. The classification accuracy produced by these approaches are inefficient since they considered only texture-based extracted features and they used some techniques that cannot extract some of the main features from the images. Also, these techniques still suffered from the issue of overfitting. In this work, handcrafted feature extraction techniques (Hu moment, Haralick textures, and color histogram) and Deep Neural Network (DNN) are employed for breast cancer multi-classification using histopathological images on the BreakHis dataset. The features extracted using the handcrafted techniques are used to train the DNN classifiers with four dense layers and Softmax. Further, the data augmentation method was employed to address the issue of overfitting. The results obtained reveal that the use of handcrafted approach as feature extractors and DNN classifiers had a better performance in breast cancer multi-classification than other approaches in the literature. Moreover, it was also noted that augmentation of data plays a key role in further improvement of classification accuracy. The proposed method achieved an accuracy score of 97.87% for 40x, 97.60% for 100x, 96.10% for 200x, and 96.84% for 400x for the magnification-dependent histopathological images classification. The results also showed that the proposed method for using the Handcrafted feature extraction method with DNN classifier had a better performance in multi-classification of breast cancer using histopathological images than most of the related works in the literature.
The novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays. The MRFO algorithm is a newly developed swarm-based metaheuristic approach that ...emulates the supportive conduct performed by manta rays in search of food. The MRFO algorithm efficiently resolves several optimization difficulties in various domains due to its ability to provide an equilibrium between global and local searches during the search procedure, resulting in nearly optimal results. Thus, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO research. First, the paper introduces the natural inspiration context of MRFO and its conceptual optimization framework, and then MRFO modifications, hybridizations, and applications across different domains are discussed. Finally, a meta-analysis of the developments of the MRFO is presented along with the possible future research directions. This study can be useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields.
•Proposed a binary version of manta ray foraging optimization algorithm for binary optimization problems.•Applied the proposed binary manta ray foraging optimization algorithm for feature selection ...using NSL-KDD and CICIDS2017 network traffic datasets.•Developed network intrusion detection model with the selected features based on random forest classifier.•Examine performance analysis and comparison of the presented model with GA, PSO, GWO, and GOA using Accuracy, Recall, Precision, F-measure, and execution time.•Statistical justification of the results achieved compared to that of GA, PSO, GWO, and GOA using t-test.
The growth within the Internet and communications areas have led to a massive surge in the dimension of network and data. Consequently, several new threats are being created and have posed difficulties for security networks to correctly discover intrusions. Intrusion Detection System (IDs) is one amongst the foremost essential events for security arrangements in network environments, and it is commonly applied to spot, track, and detect malevolent threats. Detecting intruders using metaheuristics and machine learning methodologies in recent trend offers improved discovery rate. Therefore, this paper presented an intrusion detection model using an improved Binary Manta Ray Foraging (BMRF) Optimization Algorithm based on adaptive S-shape function and Random Forest (RF) classifier. The BMFR is envisioned to identify the most relevant features and remove redundant and irrelevant ones from the intrusion detection datasets. Furthermore, the RF is used for feature evaluation and to build the intrusion detection model. The proposed method was validated and compared with other methods using two IDs benchmark datasets, which include NSL-KDD and CIC-IDS2017 datasets. The result indicates that the presented model selected 38 features with 99.6% precision, 94.3% recall, 96.9% f-measure, and 99.3% accuracy for the CIC-IDS2017 dataset. Moreover, for the NSL-KDD dataset, the presented model selected 22 features with 96.8%, 96.2%, 96.5%, and 98.8% for precision, recall, F-measure, and accuracy. In addition, a statistical significance test reveals a significance difference between the presented model and the compared methods in terms of F-measure.
The sharp rise in network attacks has been a major source of concern in cyber security, particularly that now internet usage and connectivity are in high demand. As a complement to cloud computing, ...fog computing can offer low-latency services among users of mobile and the cloud. Because of the closeness of the end users to the fog nodes and having inadequate computing resources, fog devices may get into security issues. Conventional network threats may demolish the fog computing system. The use of Intrusion Detection Systems (IDS) in conventional networks has been extensively researched, applying them directly in to the fog computing platform might become unsuitable. Nodes of the fog generate enormous quantities of data most of the time, so implementing an Intrusion detection system model over large datasets in the fog computing setting is critical. To combat some of these network attacks, an intrusion detection system (IDS), a strategic intrusion prevention innovation that can be applied in the fog computing platform utilizing machine learning techniques for network anomaly detection and network event classification threat, has proven efficient and effective. This paper presented a Genetic Algorithm Wrapper-Based feature selection and Nave Bayes for Anomaly Detection Model (GANBADM) in a Fog Environment which removes extraneous attributes to reduce time complexity while also developing an enhanced model that can predict results with greater accuracy using the Security Laboratory Knowledge Discovery Dataset (NSL-KDD). Based on the analysis, the developed system has a higher overall performance of 99.73% accuracy, with a false positive rate as low as 0.6%. This results show that the proposed GANBADM approach performs better than similar approaches in the literature.
Polymorphism of the prion protein gene (PRNP) gene determines an animal's susceptibility to scrapie. Three polymorphisms at codons 136, 154, and 171 have been linked to classical scrapie ...susceptibility, although many variants of PRNP have been reported. However, no study has investigated scrapie susceptibility in Nigerian sheep from the drier agro-climate zones. In this study, we aimed to identify PRNP polymorphism in nucleotide sequences of 126 Nigerian sheep by comparing them with public available studies on scrapie-affected sheep. Further, we deployed Polyphen-2, PROVEAN, and AMYCO analyses to determine the structure changes produced by the non-synonymous SNPs. Nineteen (19) SNPs were found in Nigerian sheep with 14 being non-synonymous. Interestingly, one novel SNP (T718C) was identified. There was a significant difference (P < 0.05) in the allele frequencies of PRNP codon 154 between sheep in Italy and Nigeria. Based on the prediction by Polyphen-2, R154H was probably damaging while H171Q was benign. Contrarily, all SNPs were neutral via PROVEAN analysis while two haplotypes (HYKK and HDKK) had similar amyloid propensity of PRNP with resistance haplotype in Nigerian sheep. Our study provides valuable information that could be possibly adopted in programs targeted at breeding for scrapie resistance in sheep from tropical regions.
Microarray data represents a valuable tool for the identification of biomarkers associated with diseases and other biological conditions. Genes, in particular, are a type of biomarker that holds ...great importance for the identification and understanding of various types of tumors, including brain, lung, and breast cancers. However, a significant portion of these cancer genes are not directly associated with the target disease, which can lead to challenges during analysis, such as increased computational complexity, poor generalization, and decreased classification accuracy, among others. To address this issue, a range of techniques and algorithms have been developed to optimize the selection of the most relevant subset of cancer genes. One highly effective approach to handle this challenge is the use of Swarm Intelligent (SI) algorithms, which are known for their efficiency and effectiveness as global search agents. In this paper, we present two distinct but related sections. First, we conduct a survey of current literature from 2019 to the present, on the use of SI algorithms for optimizing the selection of an optimal subset of cancer genes. Secondly, based on the analysis and findings from the first part, a presentation of an experimental study that evaluates the efficacy of four classical SI algorithms - Particle Swarm Optimization (PSO), Salp Swarm Optimization (SSA), Firefly Algorithm (FA), and Cuckoo Search (CS) – for optimizing the selection of relevant genes in three different cancer datasets. For the experimental study, we used the Chi-square, Mutual Information, and ANOVA filter methods to individually select 100, 200, and 500 relevant genes from the identified cancer datasets. We then passed these genes as input to each of the SI algorithms. The results of the study indicate that diverse filter-wrapper combinations can effectively address the challenge of selecting cancer genes across various datasets.
The widespread use of social media platforms such as Twitter, Instagram, Facebook, and LinkedIn have had a huge impact on daily human interactions and decision-making. Owing to Twitter's widespread ...acceptance, users can express their opinions/sentiments on nearly any issue, ranging from public opinion, a product/service, to even a specific group of people. Sharing these opinions/sentiments results in a massive production of user content known as tweets, which can be assessed to generate new knowledge. Corporate insights, government policy formation, decision-making, and brand identity monitoring all benefit from analyzing the opinions/sentiments expressed in these tweets. Even though several techniques have been created to analyze user sentiments from tweets, social media engagements include negation words and emoji elements that, if not properly pre-processed, would result in misclassification. The majority of available pre-processing techniques rely on clean data and machine learning algorithms to annotate sentiment in unlabeled texts. In this study, we propose a text pre-processing approach that takes into consideration negation words and emoji characteristics in text data by translating these features into single contextual words in tweets to minimize context loss. The proposed preprocessor was evaluated on benchmark Twitter datasets using four deep learning algorithms: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Artificial Neural Network (ANN). The results showed that LSTM performed better than the approaches already discussed in the literature, with an accuracy of 96.36%, 88.41%, and 95.39%. The findings also suggest that pre-processing information like emoji and explicit word negations aids in the preservation of sentimental information. This appears to be the first study to classify sentiments in tweets while accounting for both explicit word negation conversion and emoji translation.
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