Recent advances in nanotechnology-based modifications of micro/nano PET plastics for green energy applications.

Poly(ethylene terephthalate) (PET) plastic is an omnipresent synthetic polymer in our lives, which causes negative impacts on the ecosystem. It is crucial to take mandatory action to control the usage and sustainable disposal of PET plastics. Recycling plastics using nanotechnology offers potential solutions to the challenges associated with traditional plastic recycling methods. Nano-based degradation techniques improve the degradation process through the influence of catalysts. It also plays a crucial role in enhancing the efficiency and effectiveness of recycling processes and modifying them into value-added products. The modified PET waste plastics can be utilized to manufacture batteries, supercapacitors, sensors, and so on. The waste PET modification methods have massive potential for research, which can play major role in removing post-consumer plastic waste. The present review discusses the effects of micro/nano plastics in terrestrial and marine ecosystems and its impacts on plants and animals. Briefly, the degradation and bio-degradation methods in recent research were explored. The depolymerization methods used for the production of monomers from PET waste plastics were discussed in detail. Carbon nanotubes, fullerene, and graphene nanosheets synthesized from PET waste plastics were delineated. The reuse of nanotechnologically modified PET waste plastics for potential green energy storage products, such as batteries, supercapacitors, and sensors were presented in this review.

DNA vaccine incorporated poly (lactic-co-glycolic) acid (PLGA) microspheres offer enhanced protection against Aeromonas hydrophila infection.

Encapsulation of DNA vaccines onto carriers enhances the immunogenicity of an antigen. Specifically, biodegradable polymers offer sustained release of vaccines which is crucial for any targeted delivery approach. Poly (lactic-co-glycolic) acid (PLGA) microspheres were used to load a DNA vaccine having a targeted gene of outer membrane protein (OMP) of Aeromonas hydrophila to clone and construct a DNA vaccine using a eukaryotic expression vector system (pVAX1-OMP DNA) and delivery in Carassius auratus against A. hydrophila infection. PLGA microspheres were prepared by emulsion technique oil-in-water and characterized by a High-Resolution Scanning Electron Microscope (HR-SEM). The results of PLGA-pVAX1-OMP DNA microspheres shows that average of 100-150 µm particle size and a loading efficiency (LE) of 68.8 %. Results indicate that C. auratus fed with PLGA-pVAX1-OMP DNA microspheres revealed a significant improvement in innate immune response, which includes, myeloperoxidase activity, respiratory burst and total immunoglobulin level compared with control group fish. The immune-related gene, IL1ß, IL10, TGF, c-type, and g-type lysozyme also showed significantly higher expression after immunization. Furthermore, dietary supplementation of the PLGA-pVAX1-OMP DNA (G III) group exhibited a significantly higher survival rate (78 %) than the control group of fish. These results help us to understand the of mechanism of DNA vaccine administrated feed through PLGA nanoparticles resistance to infection by regulating systemic and innate immunity in Carassius auratus.

Fitness dependent optimizer with neural networks for COVID-19 patients.

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models.

Kurdish Handwritten character recognition using deep learning techniques.

Handwriting recognition is regarded as a dynamic and inspiring topic in the exploration of pattern recognition and image processing. It has many applications including a blind reading aid, computerized reading, and processing for paper documents, making any handwritten document searchable and converting it into structural text form. High accuracy rates have been achieved by this technology when recognizing handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. However, there is not such a system for recognizing Kurdish handwriting. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic/Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive database has been created for handwritten Kurdish characters which contain more than 40 thousand images. The created database has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system the experimental results show an acceptable recognition level. The testing results reported an 83% accuracy rate, and training accuracy reported a 96% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and comparable to the similar to other languages handwriting recognition systems.

An Indigenous, Field-Deployable, Lateral Flow Immunochromatographic Assay Rapidly Detects Infectious Myonecrosis in Shrimp, Litopenaeus vannamei.

Shrimp farming is an important socioeconomic activity worldwide. Infectious myonecrosis virus (IMNV) is an important shrimp virus responsible for significant mortality (up to 70%) in Litopenaeus vannamei. We produced recombinant capsid protein (r-IMNV31) and obtained a highly specific antibody, anti-r-IMNV31, which was used in WOAH-approved ELISA and Western blot to detect IMNV. Further, anti-r-IMNV31 was employed in an indigenously developed lateral flow immunoassay (LFA) with gold nanoparticles as a visual label. Using LFA, IMNV could be detected rapidly (20 min) from tissue homogenate with high specificity, reproducibility, and sensitivity (LOD = 103 viral particles). LFA was validated with "gold standard" qRT-PCR using 60 samples with high sensitivity (100%), specificity (86%). A Cohen's kappa coefficient of 0.86 suggested "good agreement" between LFA and qRT-PCR. With a shelf-life of ~ 1 year at ambient temperature, the use of LFA in the on-site detection of IMNV by shrimp farmers will be a reality.

Harmony search: Current studies and uses on healthcare systems.

One of the popular metaheuristic search algorithms is Harmony Search (HS). It has been verified that HS can find solutions to optimization problems due to its balanced exploratory and convergence behavior and its simple and flexible structure. This capability makes the algorithm preferable to be applied in several real-world applications in various fields, including healthcare systems, different engineering fields, and computer science. The popularity of HS urges us to provide a comprehensive survey of the literature on HS and its variants on health systems, analyze its strengths and weaknesses, and suggest future research directions. In this review paper, the current studies and uses of harmony search are studied in four main domains. (i) The variants of HS, including its modifications and hybridization. (ii) Summary of the previous review works. (iii) Applications of HS in healthcare systems. (iv) And finally, an operational framework is proposed for the applications of HS in healthcare systems. The main contribution of this review is intended to provide a thorough examination of HS in healthcare systems while also serving as a valuable resource for prospective scholars who want to investigate or implement this method.

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem.

Economic load dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to economic load dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness-dependent optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness-dependent optimizer (FDO) examines the search spaces based on the searching approach of particle swarm optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have used a fitness-dependent optimizer to solve the economic load dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of the fitness-dependent optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for the fitness-dependent optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multidimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. The empirical results obtained using the enhanced variant of the fitness-dependent optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional fitness-dependent optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced fitness-dependent optimizer. Correspondingly, various standard estimations are used to prove the stability of the fitness-dependent optimizer in phases of exploitation and exploration.

Investigation of the one-step electrochemical deposition of graphene oxide-doped poly(3,4-ethylenedioxythiophene)-polyphenol oxidase as a dopamine sensor.

In this paper, we fabricated poly(3,4-ethylenedioxythiophene) (PEDOT)-graphene oxide-polyphenol oxidase (PEDOT-GO-PPO) as a dopamine sensor. The morphology of PEDOT-GO-PPO was observed using scanning electron microscopy. Cyclic voltammetry was conducted to study the oxidation-reduction characteristics of dopamine. To optimize the pH, potential and limit of detection of dopamine, the amperometric technique was employed. The found limit of detection was 8 × 10-9 M, and the linear range was from 5 × 10-8 to 8.5 × 10-5 M. The Michaelis-Menten constant (K m) was calculated to be 70.34 µM, and the activation energy of the prepared electrode was 32.75 kJ mol-1. The electrode shows no significant change in the interference study. The modified electrode retains up to 80% of its original activity after 2 months. In the future, the biosensor can be used for the quantification of dopamine in human urine samples. The present modified electrode constitutes a tool for the electrochemical analysis of dopamine.

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection.

The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare industry by leveraging its capabilities to improve personalized healthcare services by enabling seamless communication of medical data. IoMT facilitates prompt emergency responses and provides improved quality of medical services with minimum cost. With the advancement of modern technology, progressively ubiquitous medical devices raise critical security and data privacy concerns through resource constraints and open connectivity. Vulnerabilities in IoMT devices allow unauthorized access for potential entry into healthcare and sensitive personal data. In addition, the patient may experience severe physical damage with the attack on IoMT devices. To provide security to IoMT devices and privacy to patient data, we have proposed a novel IoMT framework with the hybridization of Bayesian optimization and extreme learning machine (ELM). The proposed model derives encouraging performance with enhanced accuracy in decision-making process compared to similar state-of-the-art methods.

Banana Plant Disease Classification Using Hybrid Convolutional Neural Network.

Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.

Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.

The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease.

The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain tissue damage prevention. The study was intended to explore the segmentation effects of convolutional neural network (CNN) model on Magnetic Resonance (MR) imaging for Alzheimer's diagnosis and nursing. Specifically, 18 Alzheimer's patients admitted to Indira Gandhi Medical College (IGMC) hospital were selected as the experimental group, with 18 healthy volunteers in the Ctrl group. Furthermore, the CNN model was applied to segment the MR imaging of Alzheimer's patients, and its segmentation effects were compared with those of the fully convolutional neural network (FCNN) and support vector machine (SVM) algorithms. It was found that the CNN model demonstrated higher segmentation precision, and the experimental group showed a higher clinical dementia rating (CDR) score and a lower mini-mental state examination (MMSE) score (P < 0.05). The size of parahippocompalgyrus and putamen was bigger in the Ctrl (P < 0.05). In experimental group, the amplitude of low-frequency fluctuation (ALFF) was positively correlated with the MMSE score in areas of bilateral cingulum gyri (r = 0.65) and precuneus (r = 0.59). In conclusion, the grey matter structure is damaged in Alzheimer's patients, and hippocampus ALFF and regional homogeneity (ReHo) is involved in the neuronal compensation mechanism of hippocampal damage, and the caregivers should take an active nursing method.

Performance Evaluation of Multilayer Clustering Network Using Distributed Energy Efficient Clustering with Enhanced Threshold Protocol.

In this research, pure deterministic system has been established by a new Distributed Energy Efficient Clustering Protocol with Enhanced Threshold (DEECET) by clustering sensor nodes to originate the wireless sensor network. The DEECET is very dynamic, highly distributive, self-confessed and much energy efficient as compared to most of the other existing protocols. The MATLAB simulation provides aim proved result by means of energy dissipation being emulated in the networks lifespan for homogeneous as well as heterogeneous sensor network, which when contrasted for other traditional protocols. An enhanced result has been obtained for equitable energy dissipation for systematized networks using DEECET.

Correction to: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

[This corrects the article DOI: 10.1007/s00779-021-01541-4.].

Peptide-based epitope design on non-structural proteins of SARS-CoV-2

The SARS-CoV-2 virus has caused the severe pandemic, COVID19 and since then its been critical to produce a potent vaccine to prevent the quick transmission and also to avoid alarming deaths. Among all type of vaccines peptide based epitope design tend to outshine with respect to low cost production and more efficacy. Therefore, we started with obtaining the necessary protein sequences from NCBI database of SARS-CoV-2 virus and filtered with respect to antigenicity, virulency, pathogenicity and non-homologous nature with human proteome using different available online tools and servers. The promising proteins was checked for containing common B and T-cell epitopes. The structure for these proteins were modeled from I-TASSER server followed by its refinement and validation. The predicted common epitopes were mapped on modeled structures of proteins by using Pepitope server. The surface exposed epitopes were docked with the most common allele DRB1*0101 using the GalaxyPepDock server. The epitopes, ELEGIQYGRS from Leader protein (NSP1), YGPFVDRQTA from 3c-like proteinase (nsp5), DLKWARFPKS from NSP9 and YQDVNCTEVP from Surface glycoprotein (spike protein) are the epitopes which has more hydrogen bonds. Hence these four epitopes could be considered as a more promising epitopes and these epitopes can be used for future studies.

Quantitative correlation of lumbar foraminal stenosis with local morphological metrics.

PURPOSE: Clinical evaluation of lumbar foraminal stenosis typically includes qualitative assessments of perineural epidural fat content around the spinal nerve root and evaluation of nerve root impingement. The present study investigates the use of several morphological MRI-derived metrics as quantitative predictors of foraminal stenosis grade. METHODS: 62 adult patients that underwent lumbar spine MRI evaluation over a 1-month duration in 2018 were included in the analysis. Radiological gradings of stenosis were captured from the existing clinical electronic medical record. Clinical gradings were recorded using a 0-5 scale: 0 = no stenosis, 1 = mild stenosis, 2 = mild-moderate stenosis, 3 = moderate stenosis, 4 = moderate-severe stenosis, 5 = severe stenosis. Quantitative measures of perineural epidural fat volume, nerve root cross-sectional area, and lumbar pedicle length were derived from T1 weighted sagittal spine MRI on each side of all lumbar levels. Spearman correlations of each measured metric at each level were then computed against the stenosis gradings. RESULTS: A total of 347 volumetric segmentation and radiological foraminal stenosis grade sets were derived from the 62-subject study cohort. Statistical analysis revealed significant correlations (p < 0.001) between the volume of perineural fat and stenosis grades for all lumbar vertebral levels. CONCLUSION: The results of the study have demonstrated that segmented volumes of perineural fat predict the severity of clinically scored foraminal stenosis. This finding motivates further development of automated perineural fat segmentation methods, which could offer a quantitative imaging biometric that yields more reproducible diagnosis, assessment, and tracking of foraminal stenosis.

In vitro propagation of infectious myonecrosis virus in C6/36 mosquito cell line.

Infectious myonecrosis (IMN) is an important shrimp viral disease caused by infectious myonecrosis virus (IMNV). Based on previous reports, an attempt was made to propagate IMNV in apparently healthy C6/36 subclone of Aedes albopictus cell line. The confirmatory assays such as RT-PCR, real-time PCR and bioassay revealed that C6/36 cells were found to be susceptible to IMNV and these cells could be used easily for isolation and propagation of IMNV. The results of real-time PCR assay showed that a lower CT value of 22.25 in IMNV-infected cells was obtained on 10 day post-infection (d p.i.), whereas the higher CT value of 35.21 was obtained in IMNV-infected cells on 2 d p.i. There is no significant difference between CT values of IMNV production in vitro using C6/36 cell line and in vivo using shrimp. The IMNV propagated in C6/36 cells is capable of infecting shrimp and caused 100% mortality in shrimp. Clinical signs observed in shrimp injected with IMNV propagated in C6/36 cell line were found to be similar to naturally infected shrimp.

Draft Genome Analysis of Acinetobacter indicus Strain UBT1, an Efficient Lipase and Biosurfactant Producer.

Acinetobacter indicus strain UBT1 has shown efficient lipase (243 U ml-1) and biosurfactant (61.1% E24% emulsification and surface tension reduction to 37.7 mN m-1) production capabilities using agro-industrial waste as sole carbon source. We report here the draft genome sequence of A. indicus strain UBT1 having genome size of 2.97 Mb with 45.90% GC content. Total 2721 coding genes were predicted using National Center for Biotechnology Information-Prokaryotic Genome Annotation Pipeline (NCBI-PGAP). The whole genome shotgun project sequence data are accessible through NCBI Gene Bank under accession no. JABFOI000000000. PGAP annotation revealed the presence of the triacylglycerol lipase, phospholipase etc., that circuitously confers the oil consumption competency to the strain UBT1. Rapid Annotation using the Subsystem Technology (RAST) server used for mapping the genes to the subsystem resulted in 278 subsystem with 30% subsystem coverage. The draft genome data can be used to exploit the A. indicus strain UBT1 for its advance biotechnological application and also for further comparative genomic studies.

Evolutionary Algorithm based Ensemble Extractive Summarization for Developing Smart Medical System.

The amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing. We have considered the abstracts of the target article and its cited articles as the base summaries and a multi-objective evolutionary algorithm is applied for generating the ensemble summary of the target article. Each sentence of the base summaries is represented by a concept vector of the medical terms contained in it with the help of the Unified Modelling Language System (UMLS) tool which is widely used in various smart medical applications. These terms carry the key information of the sentence which is very useful to find out the semantic similarity among the sentences. Fitness functions of the evolutionary algorithm are mainly defined using clustering coefficient and sparsity index, the concepts of graph theory. After the convergence of the algorithm, the best solution of the final population gives the ensemble summary. Next, the semantic similarity of each sentence in the target article with the ensemble summary is calculated and the sentences which are most similar to the ensemble summary are considered as the summary of the target article. The method is applied to the articles available in the PubMed MEDLINE database system and experimental results are compared with some state of the art methods applied in the Bio-medical domain. Experimental results and comparative study based on the performance evaluation show that the method competes with some recently proposed summarization methods and outperforms others, which express the effectiveness of the proposed methodology. Different statistical tests have also been made to observe that the method is statistically significant.

Silencing of prophenoloxidase (proPO) gene in freshwater prawn, Macrobrachium rosenbergii, makes them susceptible to white spot syndrome virus (WSSV).

Prophenoloxidase (proPO) is very important to protect the invertebrates from microbial infections. Our previous studies revealed that proPO was up-regulated in WSSV-injected Macrobrachium rosenbergii and is responsible for protecting M. rosenbergii from WSSV. In order to prove this mechanism, an attempt was made in the present study to silence the proPO gene in freshwater prawn by injection of dsRNA-proPO followed by WSSV challenge. Two partial fragments of proPO with the size of 251 and 331 bp were used to synthesize dsRNA using LITMUS38i vector and E. coli. The bacterially synthesized dsRNA-proPO was used to silence proPO gene to determine its involvement in developing resistance in prawn against WSSV. In proPO gene-silenced prawn, 100% mortality was observed after WSSV challenge whereas no mortality was observed in prawn injected with WSSV alone. The WSSV infection in gene-silenced prawn was confirmed by PCR, and its propagation was quantified by ELISA and real-time PCR at different time intervals. Real-time PCR assay revealed a significant reduction in the expression of proPO gene in WSSV-challenged proPO-silenced prawn when compared to normal prawn. Level of proPO was reduced significantly in the haemolymph of proPO-silenced prawn when compared to prawn injected with PBS.