 Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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- Enhancing conservation seeding techniques: a vibration analysis of
spatially modified no-till drill in combine harvested rice fields-
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Abstract: Abstract By avoiding the burning of paddy residue and keeping significant amounts of nutrients in the soil for plants, conservation agricultural equipment like no-till drills can help to reduce air pollution. However, their performance parameters may have long-term effects on the health of tractor operators due to whole-body vibrations. In this study, the effect of selected field parameters on vibrations experienced by the operator, tractor, and spatially modified no-till drill on and off the field has been studied in the time and frequency domain. The depth of operation of drill significantly affected the power of vibrations along the X axis on the tractor whereas, engine speed affected vibrations at frequencies greater than 0.1. Lower depths and higher engine speeds contributed to high power vibrations in the system. All the selected variables for the study affected vibrations on the operator. The agricultural field caused more vibrations on the system, but the presence of straw helped dampen vibrations on the no-till drill.
PubDate: 2023-09-18
- Real-time weather data analysis by the solar fuzzy logic-based MPPT
controller-
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Abstract: Abstract The performance of a solar (PV) system with a boost converter using maximum power point tracking (MPPT) control algorithms is studied in this article. The primary goals of the latest renewable energy systems are to harvest the maximum amount of power possible from high-penetration renewable energy sources and to step up the low voltage to the required voltage level with low-power semiconductor switches. Mostly in the current study period, MPPT control technology and power converter are available. This study uses real-time data from the PV system in the area of 25.1383°N, 75.8076°E in India to analyze the boost converter or in this location, there is a change in the maximum power generated in a day due to changes in weather conditions and how much longer we can find the maximum power output due to the fluctuations. The boost converter has less switching loss and voltage stress. The FL-MPPT controller is taken into account while evaluating the effectiveness of the MPPT approach in a PV system, and the findings demonstrate that it provides the best and most steady output.
PubDate: 2023-09-18
- Deciphering the structural insights of the attachment glycoprotein of
HeV-g2, a new variant of Henipavirus through homology modeling and
molecular dynamics simulation-
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Abstract: Abstract Henipavirus harbors both pathogenic Hendra and Nipah viruses that infect humans and livestock animals. Recently, a variant of the Henipavirus, HeV-g2, has been discovered in horses and bats. Compared with the other Henipaviruses, the role of HeV-g2 proteins in host interaction is poorly understood to date. The present study focuses on the comparative sequence and structural analysis of the attachment glycoprotein of HeV-g2 with the members of the Henipavirus, which would provide valuable insights to understand its structural dynamics and functions. After retrieving the attachment glycoprotein sequence of HeV-g2, the evolutionary analysis was performed with protein sequences of members of Henipavirus, which showed a higher resemblance with Hendra and Nipah viruses confirming the possible pathogenic nature. Functionally, the EFNB2 host receptor binding site of the HeV-g2 attachment protein showed maximum amino acid residue conservation except for five residues of NiV: Ser242Thr, Gln388Lys, Phe458Tyr, Ile502Val, Asn586Ser, and single residue Val401Ile of HeV. The modeled structure of the protein when subjected to molecular simulation studies showed a similar pattern of structural stability with HeV protein, measured in terms of Root Mean Square Deviation, Root Mean Square Fluctuation (RMSF) Radius of Gyration (RoG), Solvent accessible surface area (SASA) and hydrogen bonding pattern. Our study highlighted the sequence comparability and structural dynamics of modeled attachment glycoprotein of HeV-g2 and the information obtained on the functional and structural regions can be considered for further research in order to design better inhibitors for the infection caused by this virus.
PubDate: 2023-09-14
- A review on the self-assembly of phenylalanine as the hallmark of the
neurological disorder phenylketonuria (PKU): origin to therapeutic
strategy-
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Abstract: Abstract One of the inborn errors of genetic metabolic disorder, Phenylketonuria (PKU) is a neuropsychological disorder and is the result of the presence of an elevated concentration of the amino acid phenylalanine (Phe) in the body. The physiological concentration of Phe under such a neurological disorder outstrips the level of 1.2 mM in the body. This elevated concentration of Phe results in the formation of fibrillar structures, which accumulate in various parts of the body. Various experimental spectroscopic and microscopic techniques, like, scanning electron microscopy, atomic force microscopy, fluorescence lifetime imaging microscopy, etc. can clearly identify those fibrillar aggregates. These aggregates are reported to be cytotoxic in nature, as proved by their effect on either human cell lines or mouse models. Further, these fibrillar structures have been reported to decrease the rigidity of the lipid bilayer membrane and increase the permeability of the membrane. Hence, the key factor of PKU is the formation of fibrillar structures by elevated concentrations of Phe in the body. Considering this, the suitable therapeutic protocol that can be designed should be composed of either inhibition of the formation of fibrils or the disruption of so-formed fibrils. In this regard, various antibodies, crown ethers, polyphenols like beer, lanthanide ions, etc. are known to act as potent inhibitors of the fibrils. In fine, this comprehensive understanding of the neurological disorder PKU is expected to hold promise to review the development of the disease, its mechanistic insight, and the developed therapeutic strategies, which represents the state-of-the art of PKU—origin to therapeutic strategy.
PubDate: 2023-09-14
- Presenting a novel approach based on deep learning neural network and
using brain images to diagnose Alzheimer's disease-
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Abstract: Abstract A major symptom of Alzheimer's disease is memory impairment, which is the most prevalent form of dementia. The risk of Alzheimer's disease is significantly increased by brain injury and post-traumatic stress disorder. It is imperative to develop an accurate computerized diagnosis system as a result of the large volume of neural data, and the low number of samples available. This paper aims to develop an automatic disease diagnosis system using deep-learning neural networks. A combination of two powerful neural networks has been proposed to identify the Alzheimer's disease, including a developed form of the 16-layer VGA and AlexNet models. Using Python programming language, 170 brain images of war veterans were examined in the present study. This study selected 70% of database images for training and 30% for testing. To extract features, the first step of training used deep learning with convolutional networks, and the second stage used the learned features to classify health status. Several methods presented in previous studies have been analyzed and compared with the results obtained in this study. The results show that AlexNet, with the fully connected classification layer, results in higher accuracy. Compared to the existing methods, this method has higher diagnostic accuracy, which has resulted in an increase of more than 4% in many cases.
PubDate: 2023-09-12
- Assessment of abdominal rehabilitation for diastasis recti abdominis using
ensemble autoencoder-
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Abstract: Abstract Women experience major bodily changes both during pregnancy and post-pregnancy. Diastasis Recti Abdominis (DRA) is a noticeable issue in the postpartum period among the female population in the world. Though postnatal fitness has gained attention in the recent decade, there is scarce knowledge of the abnormal condition called DRA and its consequences. In the presence of an abnormality, women feel less energetic in their daily activities and may experience fatigue in the abdominal muscles. The physical way of regaining strength in core abdominal muscles includes rehabilitation through exercises prescribed by physiotherapists. The sit-up and curl-up exercises engage the core abdominal muscles and when practiced regularly can bring back the separated recti muscles together in time. In order to bring this practice unsupervised by the physicians and monitor the pace of exercises by the patient individually, wearable Inertial measurement unit (IMU) sensors were employed. The utilization of IMU wearable sensors for DRA has been sparsely explored in literature. In this study, two groups of subjects with DRA perform the rehabilitation exercises and respective inertial measurements were observed. When the situation goes unsupervised, the effective contraction of the abdominal recti muscles and the correctness of exercises were uncertain. It’s a well-known fact that deep learning algorithms aid in determining the significant features thereby making the unsupervised classification problem more efficient. Here in this study an ensembled autoencoder neural network is implemented in which the IMU datasets were employed for the classification of correct and incorrect exercises. The latent vector generated in the autoencoder model encapsulates the inherent patterns of the input by undertaking all occurrences into a latent space. Thereby in this work, the reconstruction error generated from the autoencoder network is used to determine the correct and incorrect exercise. The ensemble approach, grouping two classes of autoencoders provides a model with higher predictivity.
PubDate: 2023-09-12
- RUSLE and AHP based soil erosion risk mapping for Jalpaiguri district of
West Bengal, India-
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Abstract: Abstract Soil erosion has been identified as one of the serious environmental issues which reduces soil fertility by removing topsoil. An integrated method has been adopted in the present study to estimate average annual soil loss in Jalpaiguri district of Indian state of West Bengal by Revised Universal Soil Loss Equation (RUSLE) model and Analytic Hierarchy Process (AHP) method. The study also includes estimation of soil loss in different land use and land cover classes and different administrative blocks. RUSLE model estimates average annual soil loss in t ha−1 year−1 by calculating combined effects of rainfall erosivity, soil erodibility, slope length and steepness, land cover and conservation practices factors. Eight geo-environmental variables, i.e., rainfall erosivity, soil erodibility, slope, relative relief, drainage density, lineament density, land use/land cover and geomorphology has been incorporated in AHP method to estimate soil erosion. The models have been validated by Receiver Operating Characteristic curve. Area Under Curve value of both RUSLE model (0.91) and AHP method (0.82) gives satisfactory result. More than 25% area of northern hilly terrain and eastern alluvial plain of Jalpaiguri district are prone to severe and very severe soil erosion. Vegetation cover shows maximum percentage of areas in very severe soil erosion. Vegetation cover of Nagrakata block and agricultural lands of Dhupguri block are most vulnerable to soil loss due to sheet and rill erosion.
PubDate: 2023-09-12
- Integrating blockchain technology and cloud services in healthcare: a
security and privacy perspective-
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Abstract: Abstract Throughout recent years, smart city development has gained considerable prominence because of its potential to enhance the living conditions of urban residents. This concept has a broad scope, covering a wide range of areas, including smart communities, smart transportation, and smart healthcare. Most smart city services, particularly healthcare-related, rely on the assessment, processing, and real-time sharing of big healthcare data to make intelligent decisions. Given the current state of the healthcare industry, it necessitates an ongoing supply of healthcare products and services, thereby enhancing its viability. Cloud-based services have made it increasingly important to develop new methods for discovering and selecting these services. Blockchain technology eliminates the need for centralized authorities in certifying and maintaining the integrity of information, facilitating transactions and exchanges of digital assets, and allowing for secure and pseudo-anonymous transactions and direct agreements between parties interacting. The technology possesses an array of key properties, including immutability, decentralization, and transparency, which may solve pressing healthcare issues, such as incomplete patient records at the point of care and difficulty accessing the information of individuals. This paper focuses on integrating blockchain technology and cloud computing in the healthcare industry to improve the security and privacy of medical data, reduce costs, and improve the efficiency of healthcare processes. Additionally, it can enable secure and direct communication between patients and healthcare providers.
PubDate: 2023-09-07
- Optimization of mechanical behavior of sandwich panel with prismatic core
based on yield and buckling constraints using the teaching-learning-based
optimization algorithm-
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Abstract: Abstract A sandwich panel is characterized by a high strength-to-weight ratio due to its unique structural design. Design variables for sandwich panels should be determined in such a way that they have the least weight while still providing the necessary strength. The teaching and learning-based optimization algorithm is used in this study to optimize corrugated-core sandwich panels’ weight. The thickness of the core and top as well as the height of the panel are considered design variables in order to minimize the weight of the panel. It was found that the panel with the hexagonal core had the highest structural efficiency. The results of the comparison indicate that the optimization algorithm based on teaching and learning is very useful and competitive with other heuristic algorithms because it uses function values directly and does not require derivatives for problems that require general optimization.
PubDate: 2023-09-07
- Automated diagnosis of bipolar depression through Welch periodogram and
machine learning techniques-
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Abstract: Abstract Bipolar depression is a chronic mood disorder that causes severe shifts in mood, behaviors and energy levels. Very few studies to date have utilized these electroencephalogram (EEG) band abnormalities for the diagnosis of bipolar depression. Considering that bipolar depression affects millions of people around the world and its correct early diagnosis is of great importance in the treatment process. Considering the importance of EEG frequency bands in investigating the neuropathology of neuropsychiatric disorders, in this study, we intend to use a Welch periodogram and support vector machine (SVM) for the automatic diagnosis of bipolar depression from EEG signals. Therefore, in this research, 20 patients with bipolar depression aged 20 to 45 years and 25 healthy individuals with the same age range were subjected to electroencephalography in a resting state. After rejecting the artifact and reducing the noise in the preprocessing stage, the Welch periodogram was applied to the EEG and five statistical features (power, mean, variance, skewness and Shannon entropy) were calculated in delta, theta, alpha, beta and gamma frequency bands. SVM with linear, polynomial, RBF and sigmoid kernels, LDA, KNN, SOM and RBF were used as classifiers. The results showed that the features selected by statistical analysis and SOM neural network could achieve good accuracy, sensitivity and specificity of 97.62, 98.70 and 97.02%, respectively, in diagnosing bipolar depression. Considering that our simple system gives promising results in diagnosing patients with bipolar depression from EEG, there is scope for further work with a larger sample size and different ages and genders.
PubDate: 2023-09-07
- Landslide susceptibility prediction using frequency ratio model: a case
study of Uttarakhand, Himalaya (India)-
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Abstract: Abstract The purpose of this study is to develop a landslide susceptibility prediction model by applying the Frequency Ratio (FR) model and remote sensing data sets for the Northern part of Uttarakhand, India. First, a landslide inventory was carried out from the interpretation of satellite images. Thereafter, the landslide inventory points were randomly separated into training and validation datasets. Subsequently, the significant landslide causative factors such as slope, lithology, lineament density, land use/land cover, drainage density, aspect, elevation, road buffer, normalized differential vegetation index (NDVI), stream power index, and topographic wetness index were identified to run the model set up. Next, applying the FR statistical model in a GIS environment for development of landslide susceptibility index map and divided into five distinct landslide susceptibility zones (very low, low, moderate, high, and very high). To validate the results, the Receiver Operating Characteristics (ROC) curve were developed to check the accurrancy of the model, and it was observed that the prediction value of the FR model was reasonably accurate (86.1% at 95% confidence level). The output LSI map would be helpful for the government and planners to map and monitor potential landslide areas and mitigate the hazards.
PubDate: 2023-09-01
- History and development of algebraic number theory
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Abstract: Abstract We briefly describe the origin and development of classical Algebraic Number Theory. We state some latest results regarding discriminants and integral bases of pure number fields as well as sextic number fields defined by irreducible trinomials with integer coefficients. A few open problems regarding class number of algebraic number fields are also mentioned.
PubDate: 2023-09-01
- Characteristics of quantum channels through evolution of quantumness
quantifiers-
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Abstract: Abstract Non-locality and non-classicality are two aspects of quantum theory which delineates it from classical counterpart. There are several ways by which non-locality and non-classicality can be quantified. In general, we call them as quantifiers of quantumness. Quantum discord and measurement induced non-locality(MIN) are examples of such kind of quantumness quantifiers in composite quantum systems. In this paper, we will geometrically investigate the evolution of these quantifiers under different quantum channels. The motivation is to characterize quantum channels, analytically, through the evolution of quantumness quantifiers.
PubDate: 2023-08-10
- Nutrient management impacts on organic carbon pool in soils under
different cropping systems in the Indo-Gangetic Plains in South Asia-
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Abstract: Abstract Nutrient management impacts the dynamics of organic carbon (C), C sequestration and various C pools in soils and sustainability of cropping systems through biomass input and organic matter addition through organic manures. We reviewed long-term field experiments on different cropping systems in the Indo-Gangetic Plains of South Asia to study the effect of balanced and imbalanced application of mineral fertilizers applied either alone or conjointly with organic manures on different soil organic C pools. Application of mineral fertilizers not only enhanced crop productivity, but also C input and accumulation in the total organic C (TOC) pool with a significant impact on C fractions of differential lability or oxidizability. Balanced use of nitrogen, phosphorus, and potassium through fertilizers alone, or conjointly with organic manures or crop residues enhanced plant-mediated C input, TOC pool, and stocks, amount of biomass C required to maintain TOC levels, net C sequestration rate, and C fractions such as permanganate oxidizable C, water-extractable organic C, microbial biomass C, and C fractions of variable oxidizability. The amount of C input (plant mediated + exogenous) added was 0.88–12.2 Mg C ha−1 year−1 in the 0–15 cm plough layer soils under rice–wheat, 1.37–6.68 Mg C ha−1 year−1 under other rice-based systems (rice–berseem/rice–wheat–jute/rice–mustard–sesame/rice–fallow–rice) and 0.51–8.07 Mg C ha−1 year−1 in soils under non-rice-based cropping systems. In general, C sequestration rate was high in soils with low initial TOC content and high silt + clay fraction. Balanced application of mineral fertilizers conjointly with organic manures enhanced the percentage of macro-aggregates (> 0.25 mm), with simultaneous decrease in the percentage of micro-aggregates (< 0.25 mm). The stable or passive C pool (less labile + recalcitrant C) was the largest pool, comprising of about 50.5–80.3% of TOC in soils under different rice–wheat systems; almost similar (44.2–80.8% of TOC) to that for maize–wheat, but higher compared with those under cotton–wheat (44.1–61.9% of TOC) cropping system. The amount of C input required to maintain TOC stocks at steady state varied between 2.30 and 4.59 Mg C ha−1 year−1 for soils under rice-based systems, as compared with 1.10 and 3.47 Mg C ha−1 year−1 in soils under non-rice-based cropping systems. On an overall basis, balanced application of mineral fertilizers conjointly with organic manures exhibited an overwhelming role in enhancing C accumulation in recalcitrant C pool, while enlarging the labile C pool for increased crop productivity due to increased nutrient cycling.
PubDate: 2023-07-24
- Current scenario and prospects of geothermal resources for sustainable
energy in India-
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Abstract: Abstract The energy deficit in India is 2752 MU with a peak power deficit of 8.66 GW in April 2022, which is high in 2022. India has a relatively considerable amount of low and medium-enthalpy hydrothermal resources, which can control the energy crisis and also environmental pollution. Globally, 10,20,887 TJ/yr of geothermal energy has been used for direct applications in the different sectors, which can replace approximately 5.96 × 107 barrels of oil equivalent energy and reduce the emission of 2.53 × 107 tonnes of CO2. In addition, various geothermal power plants with a total capacity of around 15.96 GW were installed globally till 2020. However, India is utilizing only 4007 TJ/yr of thermal energy for direct heat applications. Though the Indian geothermal resources are capable of producing 10 GW of power, India has not started commercial power production yet. The primary focus of this study is to review the geothermal energy resources, their scope and requirement in India. In addition, the strategies to overcome the challenges to harness geothermal energy in India are also discussed. The present study will help the stakeholders and researchers to propose development activities in geothermal energy extraction in India.
PubDate: 2023-07-17
DOI: 10.1007/s43538-023-00188-4
- A review on advanced nanoengineered biomaterials for chronic wound healing
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Abstract: Abstract Diabetes mellitus is a complex chronic metabolic disease that has a negative impact on patient health as well as creates a significant financial strain on healthcare systems worldwide. An unregulated molecular and cellular wound microenvironment and persistent inflammation are characteristics of chronic diabetic ulcers. Films, antimicrobial dressings, hydrogels, foams, and other biomaterials have all found uses in wound treatment. Despite many studies, there is still no “perfect” therapy for chronic wound healing, and complexities have been addressed. In this paper, we discuss the present difficulties associated with the production of biomaterials for the management and treatment of chronic wounds. Which includes a wide range of important biomaterials and their composites that accelerate angiogenesis, inhibit bacterial infection, collagen matrix deposition, and wound closure. This review also highlights other factors like oxygenation, hormones, obesity, medications, smoking, and nutrition. Finally, the future directions of biomaterials for chronic wound healing are discussed.
PubDate: 2023-07-17
DOI: 10.1007/s43538-023-00183-9
- Non-phononic superconductivity in tellurium doped Bismuth crystal
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Abstract: Abstract Superconducting transition temperature of Bismuth crystal when doped lightly with Tellurium − 0.1 atomic percent, rises by about 47 times to attain the value 25 mK from its value 0.53 mK in pure semi-metallic Bismuth crystal, experimentally observed very recently. The number density of electrons by such doping results in its enhancement by nearly two orders of magnitude, 2.8 × 1019/cc, as against 3 × 1017/cc, present in pure Bismuth crystal. However, the gas of charged fermions, electrons, with effective mass of an electron equal to 0.067 me, me being the mass of normal electron, due to elliptical Fermi surfaces of Bismuth, is still very rare and is highly quantum mechanically degenerate. It is argued that one can explain the formation of Cooper pairs, bosons, and their subsequent condensation into the superconducting phase using the non-phononic mechanism which essentially makes use of the two-body interaction (characterized by negative scattering length only) in such a dilute system, suggested by us very recently. It turns out that the value of superconducting coupling constant is equal to 0.097 indicating that the superconductivity has characteristics of Type I superconductor. The computed values of various characteristic parameters describing superconductivity such as energy gap parameter, critical magnetic field, coherence length, binding energy of Cooper pair etc. have also been reported and compared with the corresponding values in pure Bismuth. There is no isotope effect.
PubDate: 2023-07-07
DOI: 10.1007/s43538-023-00187-5
- Quantitative analysis of methanol in blood, urine, vitreous humor, and
cerebrospinal fluid by using gas chromatography-head space in two
suspected cases of methanol poisoning-
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Abstract: Abstract Methanol is a toxic alcohol and can be encountered in clinical and forensic cases. Methanol toxicity is primarily related to its metabolic products-formaldehyde and formic acid, which can cause metabolic acidosis, blindness, CNS disorders, coma, or even death. Quantitative determination and elucidation of methanol levels is crucial in clinical and forensic toxicology. Gas chromatography-headspace (GC-HS) can be used to determine methanol concentration in various biological fluids. This study reports two cases of suspected methanol poisoning. Blood, urine, vitreous humor, and cerebrospinal fluid (CSF) samples were taken for toxicological analysis during post-mortem examination. The samples were run in GC-HS for analysis of ethanol and methanol. The analysis only required 1 ml of sample and the addition of internal standard (IS). Total runtime of analysis was achieved within 20 min. Methanol was successfully detected in blood, urine, vitreous humor, and CSF using GC-HS. GC-HS is efficient for the determination of methanol concentrations, owing to its high specificity, reliability sensitivity, and reproducibility. This study also emphasizes the collection and analysis of multiple specimens, including those that are more resistant to post-mortem changes, so that they can be tested in cases where a blood sample is not readily obtainable due to alterations, putrefaction, etc. The use of an alternative specimen to the blood is of great relevance for forensic purposes.
PubDate: 2023-06-27
DOI: 10.1007/s43538-023-00179-5
- Nutricosmetics: role in health, nutrition, and cosmetics
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Abstract: Abstract Nutricosmetics by-products and constituents enhance the skin, hair, and nails' inherent attractiveness through nutrition. Nutricosmetics is emerging to be in demand in the cosmetics sector of the industry. The by-product constituents like vitamins, collagen, peptides, carotenes, minerals, proteins, and fatty acids are drawing special attention in the thorough analysis of the worldwide nutricosmetics market. The preference for formulations for cosmeceuticals made from natural products rather than synthetic substances stems from consumers' quest for new, better, and safer goods. The bioactive peptides generated from plants and microalgae are used in functional foods, medications, and cosmetics because they are known to be selective, effective, harmless, and fully absorbed by the body. Antioxidants are the most important of the substances amongst the different constituents of nutricosmetics such as beta-carotene, lycopene, and other carotenoids as well as polyphenols like anthocyanidins, catechins, etc. are the most well-known ones. Algal polysaccharides like fucoidans, alginates, and others result in distinct structural and functional characteristics. Customers today are very selective about what they consume and there is a growing need for naturally derived solutions having undesirable adverse consequences for the positive impacts on one's fitness and beauty. Numerous of these substances have been discovered to offer, among other things, antiaging, moisturizing, anti-oxidative, and other properties. This review gives an overview of the different roles of nutricosmetics derived from plant extract and algae with a focus on their role in different sectors of industries such as food, cosmeceuticals, etc.
PubDate: 2023-06-27
DOI: 10.1007/s43538-023-00181-x
- Exploratory analysis of SARS-CoV-2 omicron variant and its subvariant
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Abstract: Abstract Omicron and its subvariants have been ranked as the most common SARS-CoV-2 mutants throughout the United States and elsewhere around the world for more than a year. By December 2021, omicron had caused over a million global daily cases to grow massively, and it had begun to replicate subvariants. Several new subvariants emerged at the end of November 2022, including BF.5, BF.7, BQ.1 and BQ.1.1. This study conducted an exploratory investigation into the propagation rate of 33 SARS-CoV-2 omicron subvariants along with their ancestor that were prevalent in the United States and around the world in mid-December 2022. As a result, among omicron variant and 33 subvariants, out of 3,306,275 sequences in the United States as of mid-December 2022, the propagation rate of omicron variant and its subvariants, such as B.1.1.529, BA.2, BA.1, BA.1.10, BA.1.1, BA.5, BA.5.2, BA.5.2.1, BA.4, BA.5.1, BA.5.3, BA.5.3.1, BA.5.5, BE.1, BE.1.1, BE.1.1.1 and BQ.1 were found to be 29%, 17%, 13%, 11%, 7%, 7%, 4%, 2%, 1%, 1%, 1%, 1%, 1%, 1%, 1%, 1% and 1%. In the rest of the countries, out of 1,822,086 sequences, the propagation rate of omicron variant and its subvariants, including B.1.1.529, BA.2, BA.1, BA.1.10, BA.1.1, BA.5, BA.1.18, BA.4, BA.5.2, BA.5.2.1, BA.5.3, and BE.1 were found to be 35%, 23%, 14%, 10%, 6%, 4%, 1%, 1%, 1%, 1%, 1%, 1%, and 1%. The findings indicate that by 2023, omicron subvariants and their recombinants, including BA.1*, BA.2*, BA.5*, BE.1*, and BQ.1*, become prevalent worldwide.
PubDate: 2023-06-09
DOI: 10.1007/s43538-023-00176-8
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