Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Retracted: Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

[This retracts the article DOI: 10.1155/2021/9591670.].

Pterostilbene alleviated cerebral ischemia/reperfusion-induced blood-brain barrier dysfunction via inhibiting early endothelial cytoskeleton reorganization and late basement membrane degradation

Pterostilbene, an important analogue of the star molecule resveratrol and a novel compound naturally occurring in blueberries and grapes, exerts a significant neuroprotective effect on cerebral ischemia/reperfusion (I/R), but its mechanism is still unclear. This study aimed to follow the molecular mechanisms behind the potential protective effect of pterostilbene against I/R induced injury. For fulfilment of our aim, we investigated the protective effects of pterostilbene on I/R injury caused by middle cerebral artery occlusion (MCAO) in vivo and oxygen-glucose deprivation (OGD) in vitro. Machine learning models and molecular docking were used for target exploration and validated by western blotting. Pterostilbene significantly reduced the cerebral infarction volume, improved neurological deficits, increased cerebral microcirculation and improved blood-brain barrier (BBB) leakage. Machine learning models confirmed that the stroke target MMP-9 bound to pterostilbene, and molecular docking demonstrated the strong binding activity. We further found that pterostilbene could depolymerize stress fibers and maintain the cytoskeleton by effectively increasing the expression of the non-phosphorylated actin depolymerizing factor (ADF) in the early stage of I/R. In the late stage of I/R, pterostilbene could activate the Wnt pathway and inhibit the expression of MMP-9 to decrease the degradation of the extracellular basement membrane (BM) and increase the expression of junction proteins. In this study, we explored the protective mechanisms of pterostilbene in terms of both endothelial cytoskeleton and extracellular matrix. The early and late protective effects jointly maintain BBB stability and attenuate I/R injury, showing its potential to be a promising drug candidate for the treatment of ischemic stroke.

Deep transfer learning for IDC breast cancer detection using fast AI technique and Sqeezenet architecture

One of the most effective approaches for identifying breast cancer is histology, which is the meticulous inspection of tissues under a microscope. The kind of cancer cells, or whether they are cancerous (malignant) or non-cancerous, is typically determined by the type of tissue that is analyzed by the test performed by the technician (benign). The goal of this study was to automate IDC classification within breast cancer histology samples using a transfer learning technique. To improve our outcomes, we combined a Gradient Color Activation Mapping (Grad CAM) and image coloring mechanism with a discriminative fine-tuning methodology employing a one-cycle strategy using FastAI techniques. There have been lots of research studies related to deep transfer learning which use the same mechanism, but this report uses a transfer learning mechanism based on lightweight Squeeze Net architecture, a variant of CNN (Convolution neural network). This strategy demonstrates that fine-tuning on Squeeze Net makes it possible to achieve satisfactory results when transitioning generic features from natural images to medical images.

Formulation, Characterization, and Determination of the Rheological Profile of Loquat Compote Mespilus Germánica L. through Sustenance Artificial Intelligence

The theme of the presented study is to create a compote that is functional, inexpensive in cost, free of preservatives, and will have long shelf life, as well as to assess its rheological, sensory, and physicochemical properties. The objective was to construct a loquat compote (Mespilus germánica L.) using agar from cochayuyo (Chondracanthus chamissoi) for infants, determining its rheological profile with the addition of agar extracted from cochayuyo in three concentrations (0.10, 0.15, and 0.20) % w/w, respectively, with help of artificial intelligence (AI) pathway. Agar was withdrawn from the cochayuyo by alkaline treatment with 0.04 M NaOH, obtaining a yield of 1%. Consequently, each compote was subjected to a sensory attributes using a 5-point hedonic scale with 60 panelists (30 undergraduate students and 30 infants between 3 and 5 years of age using a graphic hedonic scale). The sensory analysis using AI as a base is applied to both adult and infant panelists determined that the compote that had as input agar from cochayuyo at a concentration of 15% had greater acceptability due to the fact that significance was reported ( $p<0.05$ ) according to Friedman’s test. The compote with the highest acceptability was subjected to proximal chemical characterization, reporting the following: moisture (64%), protein (1.68%), fat (1.01%), fiber (2.35%), ash (1.34%), and carbohydrates (29.62%). Its physicochemical characterization was also determined, reporting the following: pH (4.32), soluble solids (16° Brix), and total acidity (0.23 g malic acid/100 g compote). Finally, A Brookfield RV-DVIII ULTRA viscometer with Spindles N° 5 and 6 was used to integrate AI data gathering and use it for rheological profile assessment. The loquat compote was found to have a non-Newtonian, pseudoplastic behavior that was adjusted to the Ostwald–De Waele model with an R2 = 0.987.

Deep Neural Network-Based Novel Mathematical Model for 3D Brain Tumor Segmentation

The use of multimodal magnetic resonance imaging (MRI) to autonomously segment brain tumors and subregions is critical for accurate and consistent tumor measurement, which can help with detection, care planning, and evaluation. This research is a contribution to the neuroscience research. In the present work, we provide a completely automated brain tumor segmentation method based on a mathematical model and deep neural networks (DNNs). Each slice of the 3D picture is enhanced by the suggested mathematical model, which is then sent through the 3D attention U-Net to provide a tumor segmented output. The study includes a detailed mathematical model for tumor pixel enhancement as well as a 3D attention U-Net to appropriately separate the pixels. On the BraTS 2019 dataset, the suggested system is tested and verified. This proposed work will definitely help for the treatment of the brain tumor patient. The pixel level accuracy for tumor pixel segmentation is 98.90%. The suggested system architecture's outcomes are compared to those of current system designs. This study also examines the suggested system architecture's time complexity on various processing units with neuroscience approach.

Diagnostic Significance of 3D Automated Breast Volume Scanner in a Combination with Contrast-Enhanced Ultrasound for Breast Cancer

The incidence of cancer is increasing today, particularly lung and chest cancer. Employing novel methods to detect cancer in its earliest stages and discover painless, noninvasive treatments are urgently needed. The goal of the proposed study is to investigate the value of automated breast volume scanning (ABUS) in conjunction with contrast-enhanced ultrasonography (CEUS) in properly diagnosing breast cancer in its early stages and the effectiveness of neoadjuvant chemotherapy (NAC) in treating the disease. For the research study, information on 98 patients who had NAC and surgery in the breast surgery department of the Shaanxi Provincial Cancer Hospital has been gathered. All patients have received four cycles of NAC and underwent conventional ultrasound (HUSS), CEUS, ABUS, and pathological examination. At the same time, receiver operating characteristic (ROC) curve analysis, single factor, multiple linear regression, and other methods have also been used to analyze the diagnostic efficacy of breast cancer and NAC efficacy evaluation results. The study of this paper is totally based on the data collected from Shaanxi Provincial Cancer Hospital. The statistical and computational analyses are performed on the data collected for drawing inferences. When the findings are compared to the results of the pathological examination, HUSS has demonstrated a significant distinction between benign and malignant diagnoses with a statistical value of P < 0.05.ABUS combined with CEUS has shown no considerable differences in correlation study. Except for negative likelihood ratio, the diagnostic performance indexes of CEUS+ ABUS are substantially higher than HHUS with P < 0.05. ROC curve analysis is also performed which shows that CEUS and ABUS combination has higher precision in the analysis of breast cancer. ABUS pooled with CEUS shows great application value in the judgment of breast cancer as per the results obtained from the statistical analysis on data of 98 patients.

Understanding Gene Action, Combining Ability, and Heterosis to Identify Superior Aromatic Rice Hybrids Using Artificial Neural Network

The aromatic rice represents a smaller but independent rice collection, the quality of which is considered to be highly acceptable. Farmers are interested in growing aromatic rice due to high premium market price. The prime objective of this study was to enhance genetic improvement of aromatic rice. Combining ability analysis (GCA and SCA) and gene action are studied in a set of 7 × 7 half-diallel crosses. Twenty-one hybrids along with their seven parents were assessed in randomized complete block design. Different quantitative characters were used to estimate the magnitude of heterosis. GCA and SCA significance for all traits revealed the importance of both additive and nonadditive genetic components. Several genes determine quantitative traits, with each gene having very little impacts and being easily influenced by environmental factors. Pusa Basmati-1 and Govindobhog were the best combiners among the seven parents. In terms of per se performance, heterosis, and SCA effects on seed yield per plant and important yield qualities, the crosses BM-24 Deharadun Pahari, Baskota × Tulaipanji, and Pusa Basmati-1 × Tulaipanji may be of interest. Because of its interconnected processing properties, ANN can play a critical role in this experiment. As a result, the current study was carried out to collect data and validate it using an artificial neural network (ANN) on the combining ability, gene action, and heterosis involved in the expression of diverse fragrant rice features. Using ANN, the validation of the result was done and it was found that the overall efficiency was approximately 99%.

CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach

Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8⁺ T-cell-related host adaptive immune response and with the available positive and negative CD8⁺ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8⁺ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8⁺ TCEs from non-CD8⁺ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8⁺ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).

A Novel Model to Detect and Classify Fresh and Damaged Fruits to Reduce Food Waste Using a Deep Learning Technique

Due to a lack of efficient measures for dealing with food waste at many levels, including food supply chains, homes, and restaurants, the world’s food supply is shrinking at an alarming pace. In both homes and restaurants, overcooking and other factors are to be blamed for the majority of food that is wasted. Families are the primary source of food waste, and we sought to reduce this by identifying fresh and damaged food. In agriculture, the detection of rotting fruits becomes crucial. Despite the fact that people routinely classify healthy and rotten fruits, fruit growers find it ineffective. In contrast to humans, robots do not grow tired from doing the same thing again and again. Because of this, finding faults in fruits is a declared objective of the agricultural business in order to save labour, waste, manufacturing costs, and time spent on the process. An infected apple may infect a healthy one if the defects are not discovered. Food waste is more likely to occur as a consequence of this, which causes several problems. Input images are used to identify healthy and deteriorated fruits. Various fruits were employed in this study, including apples, bananas, and oranges. For classifying photographs into fresh and decaying fruits, softmax is used, while CNN obtains fruit image properties. A dataset from Kaggle was used to evaluate the suggested model’s performance, and it achieved a 97.14 percent accuracy rate. The suggested CNN model outperforms the current methods in terms of performance.

Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates

Zika fever is an infectious disease caused by the Zika virus (ZIKV). The disease is claiming millions of lives worldwide, primarily in developing countries. In addition to vector control strategies, the most effective way to prevent the spread of ZIKV infection is vaccination. There is no clinically approved vaccine to combat ZIKV infection and curb its pandemic. An epitope-based peptide vaccine (EBPV) is seen as a powerful alternative to conventional vaccinations because of its low production cost and short production time. Nonetheless, EBPVs have gotten less attention, despite the fact that they have a significant untapped potential for enhancing vaccine safety, immunogenicity, and cross-reactivity. Such a vaccine technology is based on target pathogen’s selected antigenic peptides called T-cell epitopes (TCE), which are synthesized chemically based on their amino acid sequences. The identification of TCEs using wet-lab experimental approach is challenging, expensive, and time-consuming. Therefore in this study, we present computational model for the prediction of ZIKV TCEs. The model proposed is an ensemble of decision trees that utilizes the physicochemical properties of amino acids. In this way a large amount of time and efforts would be saved for quick vaccine development. The peptide sequences dataset for model training was retrieved from Virus Pathogen Database and Analysis Resource (ViPR) database. The sequences dataset consist of experimentally verified T-cell epitopes (TCEs) and non-TCEs. The model demonstrated promising results when evaluated on test dataset. The evaluation metrics namely, accuracy, AUC, sensitivity, specificity, Gini and Mathew’s correlation coefficient (MCC) recorded values of 0.9789, 0.984, 0.981, 0.987, 0.974 and 0.948 respectively. The consistency and reliability of the model was assessed by carrying out the five (05)-fold cross-validation technique, and the mean accuracy of 0.97864 was reported. Finally, model was compared with standard machine learning (ML) algorithms and the proposed model outperformed all of them. The proposed model will aid in predicting novel and immunodominant TCEs of ZIKV. The predicted TCEs may have a high possibility of acting as prospective vaccine targets subjected to in-vivo and in-vitro scientific assessments, thereby saving lives worldwide, preventing future epidemic-scale outbreaks, and lowering the possibility of mutation escape.

Machine Learning and Artificial Intelligence in the Food Industry: A Sustainable Approach

The goal of this research was to look into how artificial intelligence (AI) and machine learning (ML) techniques are being used in food industry and to come up with future research directions based on that. This study investigates the articles available on several scientific platforms that link both AI and supply chain from one side and ML and food industry from the other side, using a systematic literature review methodology. The findings of this research stated that although AI and machine learning technologies are yet in their beginning, the prospective for them to enhance the performance of the food industry (FI) is quite promising. Various investigators created AI and ML-related models that were verified and found to be effective in optimising FI, and so the use of AI and ML in FI networks provides competitive advantages for improvement. Other academics suggest that AI and machine learning are both now adding value, while others believe that they are still underutilised and that their tools and methodologies can harness the overall value of the food business. According to the findings, AI and machine learning have the potential to reduce economic losses, thereby supporting the food industry's efficiency and responsiveness.

Identifying Smart Strategies for Effective Agriculture Solution Using Data Mining Techniques

Agricultural producers and enterprises face a dizzying array of decisions every day, and the many factors that influence them are incredibly complex. Agricultural planning relies heavily on accurately calculating the yields of the various crops that will be used. If you want realistic and successful solutions, data mining is an essential component. Researchers in this study are looking for ways to evaluate agricultural data and extract valuable information from the results in order to increase agricultural output. Use of the CART and random forest algorithms is a data mining technique that may be used to various datasets. It is possible to recognise the effects of various climatic and other factors on agricultural output using the MATLAB software and data mining methods, and a potential strategy is highlighted.

Artificial Neural Network-Based Identification of Associations between UCP2 and UCP3 Gene Polymorphisms and Meat Quantity Traits

In identifying mutations occurring in distinct cow breeds, genetic elements must be taken into consideration. More recently, these hereditary features have gained attention throughout the world. As in many underdeveloped nations, to bridge the deficit in molecular genetics, multiple solutions are required. The inner membrane anion carrier superfamily contains the uncoupling proteins (UCPs), vital to energy regulation. Research on heredity has shown that variations in the UCP2 and UCP3 genes are connected to obesity and metabolic syndrome. This research aimed to investigate if any mutation in the UCP 2 and UCP 3 genes are related to many characteristics in Pakistan’s three indigenous cattle breeds using artificial neural network (ANN). For better analysis, the output of the ANN model is loaded into the Primer Premier 3 software. Using polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) and sequencing, the results of this study indicated 07 variations in the exon 4 region of the UCP2 gene and 03 variants in the exon 3 area of the UCP3 gene among 215 indigenous cow breeds. The association study revealed that the g.C35G mutation in the UCP3 gene is strongly related to meat quantity characteristics such as carcass weight and drip percentage (P0.05) but not with body height or hip width ( $P>0.05$ ). Sequence analysis showed five distinct diplotypes: AA, BC, AC, CC, and CD. Cattle with the novel heterozygous diplotype BC perform better in carcass trait and drip percentage than animals with other genotypes. The study’s findings suggest that the UCP3 gene may be utilized for marker-assisted selection (MAS) and breed mixing in Pakistan cattle breeds to aid in the country’s economic growth.

Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach

Agriculture and plants, which are a component of a nation's internal economy, play an important role in boosting the economy of that country. It becomes critical to preserve plants from infection at an early stage in order to be able to treat them. Previously, recognition and classification were carried out by hand, but this was a time-consuming operation. Nowadays, deep learning algorithms are frequently employed for recognition and classification tasks. As a result, this manuscript investigates the diseases of sunflower leaves, specifically Alternaria leaf blight, Phoma blight, downy mildew, and Verticillium wilt, and proposes a hybrid model for the recognition and classification of sunflower diseases using deep learning techniques. VGG-16 and MobileNet are two transfer learning models that are used for classification purposes, and the stacking ensemble learning approach is used to merge them or create a hybrid model from the two models. This work makes use of a data set that was built by the author with the assistance of Google Images and comprises 329 images of sunflowers divided into five categories. On the basis of accuracy, a comparison is made between several existing deep learning models and the proposed model using the same data set as the original comparison.

Current Understanding of the Role of T Cells in Chikungunya, Dengue and Zika Infections

Arboviral infections such as Chikungunya (CHIKV), Dengue (DENV) and Zika (ZIKV) are a major disease burden in tropical and sub-tropical countries, and there are no effective vaccinations or therapeutic drugs available at this time. Understanding the role of the T cell response is very important when designing effective vaccines. Currently, comprehensive identification of T cell epitopes during a DENV infection shows that CD8 and CD4 T cells and their specific phenotypes play protective and pathogenic roles. The protective role of CD8 T cells in DENV is carried out through the killing of infected cells and the production of proinflammatory cytokines, as CD4 T cells enhance B cell and CD8 T cell activities. A limited number of studies attempted to identify the involvement of T cells in CHIKV and ZIKV infection. The identification of human immunodominant ZIKV viral epitopes responsive to specific T cells is scarce, and none have been identified for CHIKV. In CHIKV infection, CD8 T cells are activated during the acute phase in the lymph nodes/blood, and CD4 T cells are activated during the chronic phase in the joints/muscles. Studies on the role of T cells in ZIKV-neuropathogenesis are limited and need to be explored. Many studies have shown the modulating actions of T cells due to cross-reactivity between DENV-ZIKV co-infections and have repeated heterologous/homologous DENV infection, which is an important factor to consider when developing an effective vaccine.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety—they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets

An ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by a single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic that continues to date. Vaccination has proven to be the most effective technique, by far, for the treatment of COVID-19 and to combat the outbreak. Among all vaccine types, epitope-based peptide vaccines have received less attention and hold a large untapped potential for boosting vaccine safety and immunogenicity. Peptides used in such vaccine technology are chemically synthesized based on the amino acid sequences of antigenic proteins (T-cell epitopes) of the target pathogen. Using wet-lab experiments to identify antigenic proteins is very difficult, expensive, and time-consuming. We hereby propose an ensemble machine learning (ML) model for the prediction of T-cell epitopes (also known as immune relevant determinants or antigenic determinants) against SARS-CoV-2, utilizing physicochemical properties of amino acids. To train the model, we retrieved the experimentally determined SARS-CoV-2 T-cell epitopes from Immune Epitope Database and Analysis Resource (IEDB) repository. The model so developed achieved accuracy, AUC (Area under the ROC curve), Gini, specificity, sensitivity, F-score, and precision of 98.20%, 0.991, 0.994, 0.971, 0.982, 0.990, and 0.981, respectively, using a test set consisting of SARS-CoV-2 peptides (T-cell epitopes and non-epitopes) obtained from IEDB. The average accuracy of 97.98% was recorded in repeated 5-fold cross validation. Its comparison with 05 robust machine learning classifiers and existing T-cell epitope prediction techniques, such as NetMHC and CTLpred, suggest the proposed work as a better model. The predicted epitopes from the current model could possess a high probability to act as potential peptide vaccine candidates subjected to in vitro and in vivo scientific assessments. The model developed would help scientific community working in vaccine development save time to screen the active T-cell epitope candidates of SARS-CoV-2 against the inactive ones.