Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.

COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries

Screening microsatellite markers for establishing parental polymorphism in Indian rice (Oryza sativa L.)

The experiment was conducted to investigate the parental diversity along the rice genome and to understand and screen out the SSR markers-indicated polymorphism between two indica rice (Oryza sativa L.) cultivars. Namely K343, the most well-liked rice variety in the hill zone of the Jammu Region, and RML22, a rice line created at IRRI, Philippines. The study is to select polymorphic markers (Simple Sequence Repeat- SSR) associated with hill ecologies rice cultivars and additional research projects like gene pyramiding and background selection to recover the recurrent parent genome (RPG) in blast gene introgression in elite lines. 450 SSR markers, evenly distributed throughout the rice genome, were used to assess the parental polymorphism between these genotypes. Of these two cultivars, 51 markers (11.33%) showed polymorphism with bands in different spectrums throughout the genome. The study has been used to Marker Assisted Backcross (MAB) breeding to integrate rice blast resistance genes in the parental genotype. The pool of polymorphic markers has the potential to use in similar studies and work, with a high probability of polymorphism for the cultivars of hill ecologies, and thus increase the chance of selection of probability in marker selection

Securing the Biometric through ECG using Machine Learning Techniques

In the current era, biometrics is widely used for maintaining the security. To extract the information from the biomedical signals, biomedical signal processing is needed. One of the significant tools used for the diagnostic is electrocardiogram (ECG). The main reason behind this is the certain uniqueness in the ECG signals of the individual.  In this paper, the focus will be on distinguishing the individual on the basis of ECG signals using feature extraction approaches and the machine learning algorithms. Other than preprocessing approach, the discrete cosine transform is applied to perform the extraction. The classification between the signals of the individuals is carried out using the Support Vector Machine and K-Nearest Neighbor machine learning techniques.  The classification accuracy achieved through SVM is 87% and K-NN has achieved a classification accuracy of 96.6% with k=3. The work has shown how machine learning can be used to classify the ECG signal

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.publishedVersio

In Vitro Doubled Haploid Production of Bacterial Blight Resistant Plants from BC2F1 Plants (Ranbir Basmati X Pau148) Through Anther Culture

Doubled haploid plants are very important for the development of complete homozygous plants from heterozygous parents in one generation as they possess duplicate copy of haploid chromosome. Haploid production is easily obtained from in vitro anther culture. The present study was undertaken with the objective to develop doubled haploids using anthers for in vitro induction of callus on N6 medium supplemented with various combinations and concentrations of 2,4-dichlorophenoxy acetic acid (2,4-D) (0.5-2.5 mg/L), Kinetin (0.5-1.0 mg/L) and Naphthalene acetic acid (NAA) (2.0 mg/L) as callus induction medium (CIM). The highest callus induction frequency was obtained when N6 medium fortified with 2,4-D (2.5 mg/L), Kinetin (0.5 mg/L) and NAA (2 mg/L) of 10.07 per cent. The induced callus was sub cultured for shoot regeneration on Murashige and Skoog medium (MS) supplemented with growth regulators: Kinetin and NAA (0.5 mg/L each) in combination with BAP (0.0 - 2.5 mg/L). MS medium supplemented with NAA (0.5 mg/L), Kinetin (0.5 mg/L) and BAP (1.5 mg/L) was most responsive exhibiting regeneration frequency of 28.1 per cent which resulted in maximum regeneration of green plantlets and only 5.21 per cent of albinos. Individual plantlets were separated and immersed in liquid MS medium augmented with NAA (0.5-1.0 mg/L) and BAP (0.5-1.0 mg/L). Maximum rooting was observed in MS medium with NAA (0.5 mg/L) and BAP (1.0 mg/L). The survival rate of in-vitro raised plants was 51.51 per cent. Of these surviving plants, 21 plants were observed to have the sterility percentage above 50 percent and hence can be considered as the doubled haploid plants. Plant DH8 is susceptible and DH20 is heterozygous for gene Xa21. Two plants are susceptible for gene xa1

Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach

Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently

Genetic mapping for grain quality and yield-attributed traits in Basmati rice using SSR-based genetic map

Rice grain shape and nutritional quality traits have high economic value for commercial production of rice and largely determine the market price, besides influencing the global food demand for high-quality rice. Detection, mapping and exploitation of quantitative trait loci (QTL) associated with kernel elongation and grain quality in Basmati rice is considered as an efficient strategy for improving the kernel elongation and grain quality trait in rice varieties. Genetic information in rice for most of these traits is scanty and needed interventions through the use of molecular markers. A recombinant inbred lines (RIL) population consisting of 130 lines generated from the cross involving Basmati 370, a superior quality Basmati variety and Pusa Basmati 1121, a Basmati derived variety were used to map the QTLs for 9 important grain quality and yield related traits. Correlation studies showed that various components of yield show a significant positive relationship with grain yield. A genetic map was constructed using 70 polymorphic simple sequence repeat (SSR) markers spanning a genetic distance of 689.3 cM distributed over 12 rice chromosomes. Significant variation was observed and showed transgressive segregation for grain quality traits in RIL population. A total of 20 QTLs were identified associated with nine yield and quality traits. Epistatic interactions were also identified for grain quality related traits indicating complex genetic nature inheritance. Therefore, the identified QTLs and flanking marker information could be utilized in the marker-assisted selection to improve kernel elongation and nutritional grain quality traits in rice varieties

Harnessing Genome Editing Techniques to Engineer Disease Resistance in Plants

Modern genome editing (GE) techniques, which include clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) system, transcription activator-like effector nucleases (TALENs), zinc-finger nucleases (ZFNs) and LAGLIDADG homing endonucleases (meganucleases), have so far been used for engineering disease resistance in crops. The use of GE technologies has grown very rapidly in recent years with numerous examples of targeted mutagenesis in crop plants, including gene knockouts, knockdowns, modifications, and the repression and activation of target genes. CRISPR/Cas9 supersedes all other GE techniques including TALENs and ZFNs for editing genes owing to its unprecedented efficiency, relative simplicity and low risk of off-target effects. Broad-spectrum disease resistance has been engineered in crops by GE of either specific host-susceptibility genes (S gene approach), or cleaving DNA of phytopathogens (bacteria, virus or fungi) to inhibit their proliferation. This review focuses on different GE techniques that can potentially be used to boost molecular immunity and resistance against different phytopathogens in crops, ultimately leading to the development of promising disease-resistant crop varieties