Exploring the Phenotypic and Genetic Variabilities in Yield and Yield-Related Traits of the Diallel-Crossed F5 Population of Aus Rice.

Rice (Oryza sativa) is a major crop and a main food for a major part of the global population. Rice species have derived from divergent agro-climatic regions, and thus, the local germplasm has a large genetic diversity. This study investigated the relationship between phenotypic and genetic variabilities of yield and yield-associated traits in Aus rice to identify short-duration, high-yielding genotypes. Targeting this issue, a field experiment was carried out to evaluate the performance of 51 Aus rice genotypes, including 50 accessions in F5 generation and one short-duration check variety BINAdhan-19. The genotypes exhibited a large and significant variation in yield and its associated traits, as evidenced by a wide range of their coefficient of variance. The investigated traits, including days to maturity (DM), plant height (PH), panicle length (PL) and 1000-grain weight (TW) exhibited a greater genotypic coefficient of variation than the environmental coefficient of variation. In addition, the high broad-sense heritability of DM, PH, PL and TW traits suggests that the genetic factors significantly influence the observed variations in these traits among the F5 Aus rice accessions. This study also revealed that the grain yield per hill (GY) displayed a significant positive correlation with PL, number of filled grains per panicle (FG) and TW at both genotype and phenotype levels. According to the hierarchical and K-means cluster analyses, the accessions BU-R-ACC-02, BU-R-ACC-08 and R2-36-3-1-1 have shorter DM and relatively higher GY than other Aus rice accessions. These three accessions could be employed in the ongoing and future breeding programs for the improvement of short-duration and high-yielding rice cultivars.

Immunogenicity and safety of Mebella™ vaccine developed by Human Biologicals Institute in a Phase II/III, randomized, multicentric, non-inferiority study.

BACKGROUND: The goal of 'Measles and Rubella Strategic Framework 2021-2030' is to make "A world free from measles and rubella". To be a part of this journey, Human Biologicals Institute has developed Mebella™ vaccine, which is a lyophilized Measles and Rubella (Live) vaccine. A randomized, single blind, comparative, multicenter Phase II/III trial was conducted to compare the immunogenicity and safety of Mebella™ vaccine with MR-VAC® vaccine in healthy subjects. METHODS: A total of 888 subjects were enrolled in four age groups (222 subjects in each group) of 18 years to 49 years; 2 years to below 18 years; 12 months to below 24 months; and 9 months to below 12 months of age. The subjects were randomized in 2:1 ratio to receive single dose of either Mebella™ vaccine of Human Biologicals Institute or MR-VAC® vaccine. Immunogenicity was assessed at 42 days after the vaccination and was compared between the vaccine arms in each group. Safety was also assessed and compared between the vaccine arms during the study period. RESULTS: A total of 875 subjects completed the study out of 888 enrolled subjects. The seroprotection rates, seroconversion rates, and geometric mean titres for both Measles and Rubella components of Mebella™ vaccine were found to be comparable and non-inferior to the MR-VAC® vaccine after 42 days of vaccination. Injection site pain was the most common local adverse event reported whereas fever was the only systemic adverse event reported in both the vaccine arms. No serious adverse event was reported. CONCLUSION: It was concluded from the study results that the test vaccine, Mebella™, was immunogenic and well tolerated and was non-inferior to the comparator vaccine, MR-VAC®, when administered to healthy subjects of 9 months to 49 years of age. Clinical Trial Registry of India Identifier: CTRI/2020/07/026930.

An attempt to construct an indole-fused azabicyclo[3.3.1]nonane framework via radical cyclization.

The structural motif of an indole-fused azabicyclo[3.3.1]nonane is common in many biologically significant indole-based natural products. Because of its structural complexity, this N-bridged scaffold has become an enticing target for organic chemists. Many efficient strategies have been developed to access this ring system synthetically, but a radical approach remains unexplored. Herein, we report a radical-based strategy to construct an indole-fused azabicyclo[3.3.1]nonane structural framework. Although our initial attempt to use a Cp2TiCl-mediated radical cyclization method was found to be unsuccessful, an alternative approach using a SmI2-mediated radical cyclization protocol was effective for enabling the desired ring closure, leading to the target indole-fused azabicyclo[3.3.1]nonane ring system. The modular approach developed here can be extended with appropriate functionalities on this indole-fused N-bridged ring system to synthesize many alkaloids.

An SEQAIHR model to study COVID-19 transmission and optimal control strategies in Hong Kong, 2022.

During the COVID-19 pandemic, one of the major concerns was a medical emergency in human society. Therefore it was necessary to control or restrict the disease spreading among populations in any fruitful way at that time. To frame out a proper policy for controlling COVID-19 spreading with limited medical facilities, here we propose an SEQAIHR model having saturated treatment. We check biological feasibility of model solutions and compute the basic reproduction number ( R 0 ). Moreover, the model exhibits transcritical, backward bifurcation and forward bifurcation with hysteresis with respect to different parameters under some restrictions. Further to validate the model, we fit it with real COVID-19 infected data of Hong Kong from 19th December, 2021 to 3rd April, 2022 and estimate model parameters. Applying sensitivity analysis, we find out the most sensitive parameters that have an effect on R 0 . We estimate R 0 using actual initial growth data of COVID-19 and calculate effective reproduction number for same period. Finally, an optimal control problem has been proposed considering effective vaccination and saturated treatment for hospitalized class to decrease density of the infected class and to minimize implemented cost.

Complex dynamics and control analysis of an epidemic model with non-monotone incidence and saturated treatment.

In this manuscript, we consider an epidemic model having constant recruitment of susceptible individuals with non-monotone disease transmission rate and saturated-type treatment rate. Two types of disease control strategies are taken here, namely vaccination for susceptible individuals and treatment for infected individuals to minimize the impact of the disease. We study local as well as global stability analysis of the disease-free equilibrium point and also endemic equilibrium point based on the values of basic reproduction number R 0 . Therefore, disease eradicates from the population if basic reproduction number less than unity and disease persists in the population if basic reproduction number greater than unity. We use center manifold theorem to study the dynamical behavior of the disease-free equilibrium point for R 0 = 1 . We investigate different bifurcations such as transcritical bifurcation, backward bifurcation, saddle-node bifurcation, Hopf bifurcation and Bogdanov-Takens bifurcation of co-dimension 2. The biological significance of all types of bifurcations are described. Some numerical simulations are performed to check the reliability of our theoretical approach. Sensitivity analysis is performed to identify the influential model parameters which have most impact on the basic reproduction number of the proposed model. To control or eradicate the influence of the emerging disease, we need to control the most sensitive model parameters using necessary preventive measures. We study optimal control problem using Pontryagin's maximum principle. Finally using efficiency analysis, we determine most effective control strategy among applied controls.

Enhanced framework for COVID-19 prediction with computed tomography scan images using dense convolutional neural network and novel loss function.

Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of 'False Negatives' can put lives at risk. The primary objective is to improve the model so that it does not reveal 'Covid' as 'Non-Covid'. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the diagnosis and treatment of COVID-19.

Peptoid-Peptide Hybrid Analogs of the Enterococcus faecalis Fsr Auto-Inducing Peptide (AIP) Reveal Crucial Structure-Activity Relationships.

As multidrug-resistant bacteria become a more pressing risk to human health, alternate approaches to treating bacterial infections are being increasingly investigated. Enterococcus faecalis is an opportunistic pathogen responsible for a large percentage of secondary enterococci infections. Its pathogenicity has been shown to be largely dependent on a cell-density communication mechanism, termed quorum sensing. In this study, we conducted a systematic investigation of the lactone-containing macrocyclic signaling peptide used by E. faecalis for Fsr-mediated communication, termed gelatinase biosynthesis activating pheromone (GBAP). Specifically, through a combination of the on-resin sub-monomer and solution phase peptoid building block synthesis approaches, we successfully synthesized a library of peptoid-peptide hybrid analogs of GBAP and determined the biological effects associated with the introduction of the peptoid (N-alkyl glycine derivative) modifications. Within the macrocycle region of the peptide, as have been seen with other modifications, the F7 site was unusually tolerant toward peptoid modification, compared with other macrocyclic sites. Interestingly, within the exocyclic tail, peptoid modification at the N2 site completely abolished activity, a first for a single tail modification.

Ambient intelligence-based multimodal human action recognition for autonomous systems.

Human activity recognition can deduce the behaviour of one or more people from a set of sensor measurements. Despite its widespread applications in monitoring activities, robotics, and visual surveillance, accurate, meticulous, precise and efficient human action recognition remains a challenging research area. As human beings are moving towards the establishment of a smarter planet, human action recognition using ambient intelligence has become an area of huge potential. This work presents a method based on Bi-Convolutional Recurrent Neural Network (Bi-CRNN) -based Feature Extraction and then Random Forest classification for achieving outcomes utilizing Ambient Intelligence that are at the cutting edge of human action recognition for Autonomous Robots. The auto fusion technique used has improved fusion for utilizing and processing data from various sensors. This paper has drawn comparisons with already existing algorithms for Human Action Recognition (HAR) and tried to propose a heuristic and constructive hybrid deep learning-based algorithm with an accuracy of 94.7%.

Effect of Fear, Treatment, and Hunting Cooperation on an Eco-Epidemiological Model: Memory Effect in Terms of Fractional Derivative.

In this paper, we have studied a fractional-order eco-epidemiological model incorporating fear, treatment, and hunting cooperation effects to explore the memory effect in the ecological system through Caputo-type fractional-order derivative. We have studied the behavior of different equilibrium points with the memory effect. The proposed system undergoes through Hopf bifurcation with respect to the memory parameter as the bifurcation parameter. We perform numerical simulations for different values of the memory parameter and some of model parameters. In the numerical results, it appears that the system is exhibiting a stable behavior from a period or chaotic nature with the increase in the memory effect. The system also exhibits two transcritical bifurcations with respect to the growth rate of the prey. At low values of prey's growth, all species go to extinction, at moderate values of prey's growth, only preys (susceptible and infected) can survive, and at higher values of prey's growth, all species survive simultaneously. The paper ended with some recommendations.

New concept of biodiesel production using food waste digestate powder: Co-culturing algae-activated sludge symbiotic system in low N and P paper mill wastewater.

This paper aims to demonstrate an innovative process for the conversion of food waste digestate (FWD) powder into biofuel. The effects of different doses of FWD are investigated on microalgae-activated sludge (MAS) in treating pulp and paper mill wastewater (PPW) which generally contains insufficient nitrogen and phosphorus. FWD was added to adjust the initial N:P molar ratio in MAS at various levels (8:1 to 15:1). The highest Auxenochlorella protothecoides biomass achieved was 1.67 gL-1 at a 13.45:1 N/P molar ratio of PPW. After 10 days of cultivation, Auxenochlorella protothecoides-activated sludge system removed 91.7 %, 74.6 %, and 91.5 % of total nitrogen, phosphorus, and sCOD respectively at D0.836 gL-1 DD. The highest lipid productivity was reported as 41.27 ± 2.43 mg L-1 day-1. Fatty acid methyl ester (FAME) analysis showed the presence of an appreciable percentage of balanced saturated and unsaturated fatty acids i.e. palmitic, oleic, and linoleic acid, rendering its potential as a feedstock for biodiesel production. Activated sludge induced flocculation of Auxenochlorella protothecoides was measured. The whole process establishes an effective means of circular economy, where the secondary source of recyclable nutrients i.e. FWD will be used as a source of N and P in PPW to obtain algal biodiesel from a negative value industrial wastewater.

A systematic study of autonomous and nonautonomous predator-prey models for the combined effects of fear, refuge, cooperation and harvesting.

In the present study, we investigate the roles of fear, refuge and hunting cooperation on the dynamics of a predator-prey system, where the predator population is subject to harvesting at a nonlinear rate. We also focus on the effects of seasonal forcing by letting some of the model parameters to vary with time. We rigorously analyze the autonomous and nonautonomous models mathematically as well as numerically. Our simulation results show that the birth rate of prey and the fear of predators causing decline in it, and harvesting of predators first destabilize and then stabilize the system around the coexistence of prey and predator; if the birth rate of prey is very low, both prey and predator populations extinct from the ecosystem, and for a range of this parameter, only the prey population survive. The fear of predators responsible for increase in the intraspecific competition among the prey species and the refuge behavior of prey have tendency to stabilize the system, whereas the cooperative behavior of predators during the hunting time destroys stability in the ecosystem. Numerical investigations of the seasonally forced model showcase the appearances of periodic solution, higher periodic solutions, bursting patterns and chaotic dynamics.

Control over Multiple Nano- and Secondary Structures in Peptide Self-Assembly.

Herein, we report the rich morphological and conformational versatility of a biologically active peptide (PEP-1), which follows diverse self-assembly pathways to form up to six distinct nanostructures and up to four different secondary structures through subtle modulation in pH, concentration and temperature. PEP-1 forms twisted ß-sheet secondary structures and nanofibers at pH 7.4, which transform into fractal-like structures with strong ß-sheet conformations at pH 13.0 or short disorganized elliptical aggregates at pH 5.5. Upon dilution at pH 7.4, the nanofibers with twisted ß-sheet secondary structural elements convert into nanoparticles with random coil conformations. Interestingly, these two self-assembled states at pH 7.4 and room temperature are kinetically controlled and undergo a further transformation into thermodynamically stable states upon thermal annealing: whereas the twisted ß-sheet structures and corresponding nanofibers transform into 2D sheets with well-defined ß-sheet domains, the nanoparticles with random coil structures convert into short nanorods with α-helix conformations. Notably, PEP-1 also showed high biocompatibility, low hemolytic activity and marked antibacterial activity, rendering our system a promising candidate for multiple bio-applications.

Mathematical modelling of COVID-19: A case study of Italy.

This manuscript describes a mathematical epidemiological model of COVID-19 to investigate the dynamics of this pandemic disease and we have fitted this model to the current COVID-19 cases in Italy. We have obtained the basic reproduction number which plays a crucial role on the stability of disease free equilibrium point. Backward bifurcation with respect to the cure rate of treatment occurs conditionally. It is clear from the sensitivity analysis that the developments of self immunities with proper maintaining of social distancing of the exposed and asymptomatic individuals play key role for controlling the disease. We have validated the model by considering the COVID-19 cases of Italy and the future situations of epidemicity in Italy have been predicted from the model. We have estimated the basic reproduction number for the COVID-19 outbreak in Italy and effective reproduction number has also been studied. Finally, an optimal control model has been formulated and solved to realize the positive impacts of adapting lock down by many countries for maintaining social distancing.

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system.

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity's body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease's risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

An approach for dairy wastewater remediation using mixture of microalgae and biodiesel production for sustainable transportation.

The aim of this work is remediation of dairy wastewater (DWW) for biodiesel feedstock production using poly-microalgae cultures of four microalgae namely Chlorella minutissima (C. minutissima), Scenedesmus abundans (S. abundans), Nostoc muscorum (N. muscorum) and Spirulina sp. The poly-microalgae cultures were prepared as C. minutissima + N. muscorum (CN), C. minutissima + N. muscorum + Spirulina sp. (CNSS) and S. abundans + N. muscorum + Spirulina sp. (SNSS). Poly-microalgae culture CNSS cultivated on 70% DWW achieved 75.16, 61.37, 58.76, 84.48 and 84.58%, removals of biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and suspended solids (SS), respectively, at 12:12 h photoperiod that resulted into total biomass and lipid yield of 3.47 ± 0.07 g/L and 496.32± 0.065 mg/L. However, maximum biomass and lipid yields of 5.76 ± 0.06 and 1152.37 ± 0.065 mg/L were achieved by poly-microalgae culture CNSS cultivated on 70% DWW + 10 g/L of glucose at 18:6 h photoperiod. Fatty acid methyl ester (FAME) analysis shown presence of C14:0 (myristic acid) C16:0 (palmitic acid), C16:1 (palmitoleic acid), C18:0 (stearic acid), C18:2 (linoleic acid) and C18:3 (linolenic acid), it indicates that the lipids produced from poly-microalgae cultures are suitable for biodiesel production. Thus, poly-microalgae cultures could be more efficient than mono-microalgae cultures in the remediation of DWW and for biodiesel feedstock production.

An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images.

Coronavirus (COVID-19) is an epidemic that is rapidly spreading and causing a severe healthcare crisis resulting in more than 40 million confirmed cases across the globe. There are many intensive studies on AI-based technique, which is time consuming and troublesome by considering heavyweight models in terms of more training parameters and memory cost, which leads to higher time complexity. To improve diagnosis, this paper is aimed to design and establish a unique lightweight deep learning-based approach to perform multi-class classification (normal, COVID-19, and pneumonia) and binary class classification (normal and COVID-19) on X-ray radiographs of chest. This proposed CNN scheme includes the combination of three CBR blocks (convolutional batch normalization ReLu) with learnable parameters and one global average pooling (GP) layer and fully connected layer. The overall accuracy of the proposed model achieved 98.33% and finally compared with the pre-trained transfer learning model (DenseNet-121, ResNet-101, VGG-19, and XceptionNet) and recent state-of-the-art model. For validation of the proposed model, several parameters are considered such as learning rate, batch size, number of epochs, and different optimizers. Apart from this, several other performance measures like tenfold cross-validation, confusion matrix, evaluation metrics, sarea under the receiver operating characteristics, kappa score and Mathew's correlation, and Grad-CAM heat map have been used to assess the efficacy of the proposed model. The outcome of this proposed model is more robust, and it may be useful for radiologists for faster diagnostics of COVID-19.

Study of SEIR epidemic model and scenario analysis of COVID-19 pandemic.

In recent times, the Coronavirus disease (caused by COVID-19) is evidently observed to be the extremely contagious one with high fatality rate worldwide. In March 2020, the disease was declared a "global pandemic" by the World Health Organization (WHO). So far, there is no known/effective vaccine or medicine. In this paper, we propose and analyze an SEIR compartment model. We also compare and analyze the case study of India and Brazil. The model system is discussed by using MATLAB (2018a) software and the numerical results are verified graphically.

Global dynamics and control strategies of an epidemic model having logistic growth, non-monotone incidence with the impact of limited hospital beds.

In this paper, we have considered a deterministic epidemic model with logistic growth rate of the susceptible population, non-monotone incidence rate, nonlinear treatment function with impact of limited hospital beds and performed control strategies. The existence and stability of equilibria as well as persistence and extinction of the infection have been studied here. We have investigated different types of bifurcations, namely Transcritical bifurcation, Backward bifurcation, Saddle-node bifurcation and Hopf bifurcation, at different equilibrium points under some parametric restrictions. Numerical simulation for each of the above-defined bifurcations shows the complex dynamical phenomenon of the infectious disease. Furthermore, optimal control strategies are performed using Pontryagin's maximum principle and strategies of controls are studied for two infectious diseases. Lastly using efficiency analysis we have found the effective control strategies for both cases.

Understanding the roles of osmolytes for acclimatizing plants to changing environment: a review of potential mechanism.

Abiotic stresses are significant environmental issues that restrict plant growth, productivity, and survival while also posing a threat to global food production and security. Plants produce compatible solutes known as osmolytes to adapt themselves in such changing environment. Osmolytes contribute to homeostasis maintenance, provide the driving gradient for water uptake, maintain cell turgor by osmotic adjustment, and redox metabolism to remove excess level of reactive oxygen species (ROS) and reestablish the cellular redox balance as well as protect cellular machinery from osmotic stress and oxidative damage. Perceiving the mechanisms how plants interpret environmental signals and transmit them to cellular machinery to activate adaptive responses is important for crop improvement programs to get stress-tolerant varieties. A large number of studies conducted in the last few decades have shown that osmolytes accumulate in plants and have strong associations with abiotic stress tolerance. Production of abundant osmolytes is needed for tolerance in many plant species. In addition, transgenic plants overexpressing genes for different osmolytes showed enhanced tolerance to various abiotic stresses. Many important aspects of their mechanisms of action are yet to be largely identified, especially regarding the relevance and relative contribution of specific osmolytes to the stress tolerance of a given species. Therefore, more efforts and resources should be invested in the study of the abiotic stress responses of plants in their natural habitats. The present review focuses on the possible roles and mechanisms of osmolytes and their association toward abiotic stress tolerance in plants. This review would help the readers in learning more about osmolytes and how they behave in changing environments as well as getting an idea of how this knowledge could be applied to develop stress tolerance in plants.

COVID-19 pandemic in India: a mathematical model study.

The present novel coronavirus (SARS-CoV-2) infection has created a global emergency situation by spreading all over the world in a large scale within very short time period. But there is no vaccine, anti-viral medicine for such infection. So at this moment, a major worldwide problem is that how we can control this pandemic. On the other hand, India is high population density country, where the coronavirus infection disease (COVID-19) has started from 1 March 2020. Due to high population density, human to human social contact rate is very high in India. So controlling pandemic COVID-19 in early stage is very urgent and challenging problem of India. Mathematical models are employed to study the disease dynamics, identify the influential parameters and access the proper prevention strategies for reduction outbreak size. In this work, we have formulated a deterministic compartmental model to study the spreading of COVID-19 and estimated the model parameters by fitting the model with reported data of ongoing pandemic in India. Sensitivity analysis has been done to identify the influential model parameters. The basic reproduction number has been estimated from actual data and the effective basic reproduction number has been studied on the basis of reported cases. Some effective preventive measures and their impact have also been studied. Prediction are given on the future trends of the virus transmission under some control measures. Finally, the positive measures to control the disease have been summarized in the conclusion section.