Stress mitigation strategies of plant growth-promoting rhizobacteria: Plant growth-promoting rhizobacteria mechanisms

One of the major challenges that the world is facing currently is the inadequate amount of food production with high nutrient content in accordance with the increase in population size. Moreover, availability of cultivable area with fertile soil is reducing day by day owing to ever increasing population. Further, water scarcity and expensive agricultural equipment have led to the use of agrochemicals and untreated water. Excessive use of chemical fertilizers to increase crop yield have resulted in deleterious effects on the environment, health and economy, which can be overcome to a great extent by employing biological fertilizers. There are various microbes that grows in the rhizospheric region of plants known as plant growth-promoting rhizobacteria (PGPR). PGPR act by direct and indirect modes to stimulate plant growth and improve stress reduction in plants. PGPRs are used for potential agriculture practices having a wide range of benefits like increase in nutrients content, healthy growth of crops, production of phytohormones, prevention from heavy metal stress conditions and increase in crop yield. This review reports recent studies in crop improvement strategies using PGPR and describes the mechanisms involved. The potential mechanisms of PGPR and its allies pave the way for sustainable development towards agriculture and commercialization of potential bacteria

Highly Efficient Mesoporous Carbonaceous CeO2Catalyst for Dephosphorylation

Phosphorus is fast becoming a critical element, as the global supply and demand are reaching unsustainable levels. Herein, the synthesis, characterization, and applicability of a novel biomass-derived mesoporous carbonaceous material decorated with CeO2 (CeO2-S400) as an efficient catalyst for the dephosphorylation of 4-nitrophenyl phosphate disodium salt hexahydrate are reported. The presence and distribution of CeO2 are evidenced by inductively coupled plasma mass spectrometry (ICP-MS) (118.7 mg/g), high-resolution transmission electron microscopy (HRTEM), and energy dispersive X-ray (EDX) mapping. The apparent rate constant for the efficient catalysis of 4-nitrophenyl phosphate disodium salt hexahydrate was 0.097 ± 0.01 for CeO2-ES and 0.15 ± 0.03 min-1 for CeO2-S400, which followed first-order kinetics. Rate constants normalized by the catalytic loading (km) were 80.84 and 15.00 g-1 min-1 for CeO2-ES and CeO2-S400, respectively, and the normalized rate constants with respect to surface area were 3.38 and 0.04 m-2 min-1 for CeO2-ES and CeO2-S400, respectively. This indicates that the presence of CeO2 nanoparticles has a catalytic effect on the dephosphorylation reaction

Nonlinear dynamics for the spread of pathogenesis of COVID-19 pandemic

Coronaviruses did not invite attention at a global level and responsiveness until the series of 2003-SARS contagion followed by year-2012 MERS plus, most recently, 2019-nCoV eruptions. SARS-CoV & MERS-CoV are painstaking, extremely pathogenic. Also, very evidently, both have been communicated from bats to palm-civets & dromedary camels and further transferred ultimately to humans. No country has been deprived of this viral genomic contamination wherever populaces reside and are interconnected. This study aimed to develop a mathematical model for calculating the transmissibility of this viral genome. The analysis aids the study of the outbreak of this Virus towards the other parts of the continent and the world. The parameters such as population mobility, natural history, epidemiological characteristics, and the transmission mechanism towards viral spread when considered into crowd dynamism result in improved estimation. This article studies the impact of time on the amount of susceptible, exposed, the infected person taking into account asymptomatic and symptomatic ones; recovered i.e., removed from this model and the virus particles existing in the open surfaces. The transition from stable phase to attractor phase happens after 13 days i.e.; it takes nearly a fortnight for the spread to randomize among people.Further, the pandemic transmission remains in the attractor phase for a very long time if no control measures are taken up. The attractor-source phase continues up to 385 days i.e., more than a year, and perhaps stabilizes on 386th day as per the Lyapunov exponent's analysis. The time series helps to know the period of the Virus's survival in the open sources i.e. markets, open spaces and various other carriers of the Virus if not quarantined or sanitized. The Virus cease to exist in around 60 days if it does not find any carrier or infect more places, people etc. The changes in LCEs of all variables as time progresses for around 400 days have been forecasted. It can be observed that phase trajectories indicate how the two variables interact with each other and affect the overall system's dynamics. It has been observed that for exposed and asymptomatically infected (y–z), as exposed ones (y) change from 0 to 100 the value of asymptomatically infected (z) increased upto around 58, at exposed ones (y) = 100, asymptomatically infected (z) has two values as 58 and 10 i.e. follows bifurcation and as exposed ones (y) changes values upto 180, the value of asymptomatically infected (z) decreases to 25 so for exposed ones (y) from 100 to 180, asymptomatically infected (z) varies from 58 to 25 to 10 follows bifurcation. Also, phase structures of exposed-symptomatically infected (y–u), exposed-removed (y–v), exposed-virus in the reservoir (y–w), asymptomatically infected-removed (z–v), symptomatically infected-removed (u–v) specifically depict bifurcations in various forms at different points. In case of asymptomatically infected-virus in the reservoir (z–w), at asymptomatically infected (z) = 10, the value of viruses in the reservoir (w) = 50, then as asymptomatically infected (z) increases to upto around 60. At this point, removed ones (v) increase from 50 to 70 and asymptomatically infected (z) decrease to 20 i.e., crosses the same value twice, which shows its limiting is known as limit cycle behavior and both the values tend to decrease towards zero. It shows a closed-loop limit cycle. Today, there has been no scientific revolution in the development of vaccination, nor has any antiviral treatment been successful, resulting in lack of its medication. Based on the phases, time series, and complexity analysis of the model's various parameters, it is studied to understand the variation in this pandemic's scenario

F-Box Proteins in Rice. Genome-Wide Analysis, Classification, Temporal and Spatial Gene Expression during Panicle and Seed Development, and Regulation by Light and Abiotic Stress

F-box proteins constitute a large family in eukaryotes and are characterized by a conserved F-box motif (approximately 40 amino acids). As components of the Skp1p-cullin-F-box complex, F-box proteins are critical for the controlled degradation of cellular proteins. We have identified 687 potential F-box proteins in rice (Oryza sativa), the model monocotyledonous plant, by a reiterative database search. Computational analysis revealed the presence of several other functional domains, including leucine-rich repeats, kelch repeats, F-box associated domain, domain of unknown function, and tubby domain in F-box proteins. Based upon their domain composition, they have been classified into 10 subfamilies. Several putative novel conserved motifs have been identified in F-box proteins, which do not contain any other known functional domain. An analysis of a complete set of F-box proteins in rice is presented, including classification, chromosomal location, conserved motifs, and phylogenetic relationship. It appears that the expansion of F-box family in rice, in large part, might have occurred due to localized gene duplications. Furthermore, comprehensive digital expression analysis of F-box protein-encoding genes has been complemented with microarray analysis. The results reveal specific and/or overlapping expression of rice F-box protein-encoding genes during floral transition as well as panicle and seed development. At least 43 F-box protein-encoding genes have been found to be differentially expressed in rice seedlings subjected to different abiotic stress conditions. The expression of several F-box protein-encoding genes is also influenced by light. The structure and function of F-box proteins in plants is discussed in light of these results and the published information. These data will be useful for prioritization of F-box proteins for functional validation in rice

Mapping Cancer in Africa:A Comprehensive and Comparable Characterization of 34 Cancer Types Using Estimates From GLOBOCAN 2020

Objective: Cancer incidence and mortality rates in Africa are increasing, yet their geographic distribution and determinants are incompletely characterized. The present study aims to establish the spatial epidemiology of cancer burden in Africa and delineate the association between cancer burden and the country-level socioeconomic status. The study also examines the forecasts of the cancer burden for 2040 and evaluates infrastructure availability across all African countries. Methods: The estimates of age, sex, and country-specific incidence and mortality of 34 neoplasms in 54 African countries, were procured from GLOBOCAN 2020. Mortality-to-incidence ratio (MIR) was employed as a proxy indicator of 5-year survival rates, and the socioeconomic development of each country was measured using its human development index (HDI). We regressed age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), and MIR on HDI using linear regression model to determine the relationship between cancer burden and HDI. Maps were generated for each cancer group for each country in Africa. The data about the cancer infrastructure of African countries were extracted from the WHO Cancer Country Profiles. Results: In Africa, an estimated 1.1 million new cases [95% uncertainty intervals (UIs) 1.0 – 1.3 million] and 711,429 [611,604 – 827,547] deaths occurred due to neoplasms in 2020. The ASIR was estimated to be 132.1/100,000, varying from 78.4/100,000 (Niger) to 212.5/100,000 (La Réunion) in 2020. The ASMR was 88.8/100,000 in Africa, ranging from 56.6/100,000 in the Republic of the Congo to 139.4/100,000 in Zimbabwe. The MIR of all cancer combined was 0.64 in Africa, varying from 0.49 in Mauritius to 0.78 in The Gambia. HDI had a significant negative correlation with MIR of all cancer groups combined and main cancer groups (prostate, breast, cervical and colorectal). HDI explained 75% of the variation in overall 5-year cancer survival (MIR). By 2040, the burden of all neoplasms combined is forecasted to increase to 2.1 million new cases and 1.4 million deaths in Africa. Conclusion: High cancer mortality rates in Africa demand a holistic approach toward cancer control and management, including, but not limited to, boosting cancer awareness, adopting primary and secondary prevention, mitigating risk factors, improving cancer infrastructure and timely treatment

Target association rule mining to explore novel paediatric illness patterns in emergency settings

Background and aimsTo assess the hospitalized sick children admitted to the pediatric emergency department (ED) and to find new patterns of clinical and laboratory attributes using association rule mining (ARM).MethodsIn this observational study, 158 children with median (IQR) age 11 months and a PRISM III score of 5 (2-9) were enrolled. Hotspot data mining method was applied to assess clinical attributes, lab investigations and pre-defined outcome parameters of children and their association in sick hospitalized children aged 1 month to 12 years.ResultsWe obtained 30 rules with value for outcome as discharge is given attributes as follows: duration of hospitalization > 4 days, lactate > 1.2 mmol/L, platelet = 3.67/mu L, dur\_ventil = 0 h, serum K = 5.2 mmol/L, SBP = 120 mmHg, pCO(2) = 41.9 mmHg, PaO2 = 163 mmHg, age = 92 months, heart rate > 114-159 per minute, temperature > 98 degrees F, GCS (Glasgow Coma Scale) > 7-14, gas K = 4.14 mmol/L, gas Na = 138.1 mmol/L, BUN (Blood Urea Nitrogen) = 18.69 mg/dL, Diagnosis > 1-718, Creatinine = 1.2 mg/dL, serum Na = 148 mmol/L, shock = 2, Glucose = 144 mg/dL, Mg(i) > 0.23 meq/L, BUN > 6.54 mg/dL.ConclusionARM is an effective data analysis technique to find meaningful patterns using clinical features with actual numbers in pediatric critical illness. It can prove to be important while analysing the association of clinical attributes with disease pattern, its features, and therapeutic or intervention success patterns