Showing papers by "Manoj Kumar published in 2021"

Metabolomics and Molecular Approaches Reveal Drought Stress Tolerance in Plants.

Abstract: Metabolic regulation is the key mechanism implicated in plants maintaining cell osmotic potential under drought stress. Understanding drought stress tolerance in plants will have a significant impact on food security in the face of increasingly harsh climatic conditions. Plant primary and secondary metabolites and metabolic genes are key factors in drought tolerance through their involvement in diverse metabolic pathways. Physio-biochemical and molecular strategies involved in plant tolerance mechanisms could be exploited to increase plant survival under drought stress. This review summarizes the most updated findings on primary and secondary metabolites involved in drought stress. We also examine the application of useful metabolic genes and their molecular responses to drought tolerance in plants and discuss possible strategies to help plants to counteract unfavorable drought periods.

SNF1-related protein kinase 1: the many-faced signaling hub regulating developmental plasticity in plants.

Abstract: The Snf1-related protein kinase 1 (SnRK1) is the plant homolog of the heterotrimeric AMP-activated protein kinase/sucrose non-fermenting 1 (AMPK/Snf1), which works as a major regulator of growth under nutrient-limiting conditions in eukaryotes. Along with its conserved role as a master regulator of sugar starvation responses, SnRK1 is involved in controlling the developmental plasticity and resilience under diverse environmental conditions in plants. In this review, through mining and analyzing the interactome and phosphoproteome data of SnRK1, we are highlighting its role in fundamental cellular processes such as gene regulation, protein synthesis, primary metabolism, protein trafficking, nutrient homeostasis, and autophagy. Along with the well-characterized molecular interaction in SnRK1 signaling, our analysis highlights several unchartered regions of SnRK1 signaling in plants such as its possible communication with chromatin remodelers, histone modifiers, and inositol phosphate signaling. We also discuss potential reciprocal interactions of SnRK1 signaling with other signaling pathways and cellular processes, which could be involved in maintaining flexibility and homeostasis under different environmental conditions. Overall, this review provides a comprehensive overview of the SnRK1 signaling network in plants and suggests many novel directions for future research.

Plant-Growth-Promoting Rhizobacteria Emerging as an Effective Bioinoculant to Improve the Growth, Production, and Stress Tolerance of Vegetable Crops.

Abstract: Vegetable cultivation is a promising economic activity, and vegetable consumption is important for human health due to the high nutritional content of vegetables. Vegetables are rich in vitamins, minerals, dietary fiber, and several phytochemical compounds. However, the production of vegetables is insufficient to meet the demand of the ever-increasing population. Plant-growth-promoting rhizobacteria (PGPR) facilitate the growth and production of vegetable crops by acquiring nutrients, producing phytohormones, and protecting them from various detrimental effects. In this review, we highlight well-developed and cutting-edge findings focusing on the role of a PGPR-based bioinoculant formulation in enhancing vegetable crop production. We also discuss the role of PGPR in promoting vegetable crop growth and resisting the adverse effects arising from various abiotic (drought, salinity, heat, heavy metals) and biotic (fungi, bacteria, nematodes, and insect pests) stresses.

ACC deaminase producing plant growth promoting rhizobacteria enhance salinity stress tolerance in Pisum sativum

Abstract: Salinity stress is one of the most serious environmental stresses which limit plant growth, development and productivity. In this study, we screened 25 bacterial isolates based on the biochemical activity of ACC deaminase. Two potent PGPR namely Bacillus marisflavi (CHR JH 203) and Bacillus cereus (BST YS1_42) having the highest ACC deaminase (ACCD) activity were selected for further analyses such as polymerase chain reaction (PCR), salt tolerance assay, expression analysis, antioxidant assay, etc. The structural gene for ACCD activity was further confirmed by PCR showing the amplicon size 800 bp. The acdS positive isolates exhibited optimum growth at 3% w/v (NaCl), indicating its ability to survive and thrive in induced saline soil. Inoculation of acdS+ strain on pea plants was found to be efficient and ameliorated the induced NaCl-stress by enhancing the various parameters like plant-biomass, carbohydrates, reducing sugars, protein, chlorophylls, phenol, flavonoids content and increasing antioxidants enzymes levels in plants. Moreover, the expression of ROS scavenging genes (PsSOD, PsCAT, PsPOX, PsNOS, PsAPX, PsChla/bBP), defense genes and cell rescue genes (PsPRP, PsMAPK, PsFDH) were analyzed. Inoculated plants exhibited a higher gene expression level and salt tolerance under 1%NaCl concentration. Thus, our results indicate that CHR JH 203 and BST YS1_42 strain showed the highest plant growth-promoting attributes could be used as bio-inoculants for crops under saline stress in the field towards sustainable crop development.

Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

Abstract: Congestion in wireless sensor networks (WSNs) is an unavoidable issue in today’s scenario, where data traffic increased to its aggregated capacity of the channel. The consequence of this turns in to overflowing of the buffer at each receiving sensor nodes which ultimately drops the packets, reduces the packet delivery ratio, and degrades throughput of the network, since retransmission of every unacknowledged packet is not an optimized solution in terms of energy for resource-restricted sensor nodes. Routing is one of the most preferred approaches for minimizing the energy consumption of nodes and enhancing the throughput in WSNs, since the routing problem has been proved to be an NP-hard and it has been realized that a heuristic-based approach provides better performance than their traditional counterparts. To tackle all the mentioned issues, this paper proposes an efficient congestion avoidance approach using Huffman coding algorithm and ant colony optimization (ECA-HA) to improve the network performance. This approach is a combination of traffic-oriented and resource-oriented optimization. Specially, ant colony optimization has been employed to find multiple congestion-free alternate paths. The forward ant constructs multiple congestion-free paths from source to sink node, and backward ant ensures about the successful creation of paths moving from sink to source node, considering energy of the link, packet loss rate, and congestion level. Huffman coding considers the packet loss rate on different alternate paths discovered by ant colony optimization for selection of an optimal path. Finally, the simulation result presents that the proposed approach outperforms the state of the art approaches in terms of average energy consumption, delay, and throughput and packet delivery ratio.

Watershed prioritization for soil erosion mapping for the Lesser Himalayan Indian basin using PCA, WSA method in conjunction with morphometric parameters and GIS-based approach

Abstract: Watersheds in the subtropical Himalayan basins are highly prone to land degradation due to deforestation, landslides, intensive agriculture, population pressure and overgrazing, in particular, where various fluvial and denudation processes occur. It is important to assess the magnitude of problem and to understand the erosion process under normal conditions, so that effective measures can be implemented. Therefore, the study selected Kalsa watershed from the Lesser Himalayan region, where soil erosion is more prominent. Regarding this issue, to identify the hot spot of soil erosion of the basin, watershed prioritization methods using advanced geographical information system and remote sensing techniques integrated with weighted sum analysis (WSA) and principal component analysis (PCA). In addition, a comparison has been made to evaluate the performance of these models. The study considered sixteen different morphometric parameters, including linear (rho coefficient, stream frequency, drainage density, length of overland flow, drainage texture and constant of channel maintenance); landscape (relative relief, relief ratio, basin slope and ruggedness number); and shape (elongation ratio, form factor, circulatory ratio and compactness coefficient). Both the methods PCA and WSA indicate the same results showing high priority, meaning the outlet watersheds have high priority. The sub-watersheds in the north-eastern part have the lowest priority. The results also show that the length overland flow, relative relief, basin relief ratio and hypsometric integral are the most important indicators. The sub-watersheds prioritize high ranks, medium ranks and low ranks out of 10 sub-watersheds covering about 45.32%, 27.78% and 26.90% area of the Kalsa River watershed, respectively. This study will help regional planners, farmers and governments take more detailed decisions to propose efficient soil erosion control measures and conservation priorities of the watershed. The study findings have implications for sustainable land management and conservation goal targets (target 2.3 and 2.4; target 3.9; target 13.1, 13.2 and 13.3; target 15.3 and 15.4), which finally helps to achieve the United Nation’s 2030 Agenda for Sustainable Development.

Synthesis and studies of thiazolidinedione-isatin hybrids as α-glucosidase inhibitors for management of diabetes.

Abstract: Aim: Keeping in view the side effects associated with clinically used α-glucosidase inhibitors, novel thiazolidinedione–isatin hybrids were synthesized and evaluated by in vitro, in vivo and in sil...

Effect of pre-existing diseases on COVID-19 infection and role of new sensors and biomaterials for its detection and treatment.

Abstract: The entire world is suffering from a new type of viral disease, occurred by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The present article briefly discussed the genome sequencing and interaction of host cells with SARS-CoV-2. The influence of pre-existing diseases such as diabetes, heart disease and age of the patients on COVID-19 infection is reviewed. The possible treatments of SARS-CoV-2 including antiviral drugs, Chinese traditional treatment and plasma therapy are elaborately discussed. The proper vaccine for COVID-19 is not available till date. However, the trials of pre-existing antiviral vaccines such as, chloroquine/hydroxychloroquine, remdesivir, ritonavir and lopinavir and their consequences are briefly presented. Further, the importance of new materials and devices for the detection and treatment of COVID-19 has also been reviewed. The polymerase chain reaction (PCR)-based, and non-PCR based devices are used for the detection of COVID-19 infection. The non-PCR based devices provide rapid results as compared to PCR based devices.

Integrated bioinformatic analysis identifies UBE2Q1 as a potential prognostic marker for high grade serous ovarian cancer.

Abstract: High grade serous ovarian cancer (HGSOC) accounts for nearly 60% of total cases of epithelial ovarian cancer. It is the most aggressive subtype, which shows poor prognosis and low patient survival. For better management of HGSOC patients, new prognostic biomarkers are required to facilitate improved treatment strategies and ensure suitable healthcare decisions. We performed genome wide expression analysis of HGSOC patient samples to identify differentially expressed genes (DEGs) using R based Limma package, Clust and other statistical tools. The identified DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to identify co-expression patterns of relevant genes. Module trait and gene ontology analyses were performed to establish important gene co-expression networks and their biological functions. Overlapping the most relevant DEG cluster 4 with prominent WGCNA cyan module identified strongest correlation of UBE2Q1 with ovarian cancer and its prognostic significance on survival probability of ovarian cancer patients was investigated. The predictive value of UBE2Q1 as a potential biomarker was analysed by correlating its expression with 12-months relapse free survival of patients in response to platin/taxane, the standard first-line chemotherapy for ovarian cancer, and analysing area under the ROC curve. An integrated gene expression analysis and WGCNA, identified UBE2Q1 as a potential prognostic marker associated with poor relapse-free survival and response outcome to platin/taxane treatment of patients with high grade serous ovarian cancer. Our study identifies a potential UBE2Q1 – B4GALT3 functional axis in ovarian cancer, where only the E2 conjugating enzyme showed a poor prognostic impact on the disease.

We Asked the Experts: The WHO Surgical Safety Checklist and the COVID-19 Pandemic: Recommendations for Content and Implementation Adaptations.

Abstract: As surgical systems are forced to adapt and respond to new challenges, so should the patient safety tools within those systems. We sought to determine how the WHO SSC might best be adapted during the COVID-19 pandemic. 18 Panelists from five continents and multiple clinical specialties participated in a three-round modified Delphi technique to identify potential recommendations, assess agreement with proposed recommendations and address items not meeting consensus. From an initial 29 recommendations identified in the first round, 12 were identified for inclusion in the second round. After discussion of recommendations without consensus for inclusion or exclusion, four additional recommendations were added for an eventual 16 recommendations. Nine of these recommendations were related to checklist content, while seven recommendations were related to implementation. This multinational panel has identified 16 recommendations for sites looking to use the surgical safety checklist during the COVID-19 pandemic. These recommendations provide an example of how the SSC can adapt to meet urgent and emerging needs of surgical systems by targeting important processes and encouraging critical discussions.

Calcium Oxide Supported on Coal Fly Ash (CaO/CFA) as an Efficient Catalyst for Biodiesel Production from Jatropha curcas Oil

Abstract: This work investigated the potentiality of calcium oxide supported on coal fly ash (CaO/CFA) as a solid catalyst for biodiesel synthesis from Jatropha oil (JO). Synthesized CaO/CFA catalyst at various precursor salt concentrations was investigated and exhibited good activity for transesterification reaction when the 40% wt/vol. concentration of precursor salt solution was loaded on coal fly ash. The optimal CaO/CFA catalyst was characterized by various spectroscopic techniques. Taguchi design approach was utilized to find the optimum condition for the biodiesel production process. The biodiesel yield was mainly affected by catalyst amount, temperature, and methanol/JO molar ratio among the studied process variables. At the optimum catalyst amount of 0.5 wt.%, methanol/JO molar ratio of 12:1, temperature of 60 °C, and time of 1 h, the maximum predicted and observed biodiesel yields were 95.64% and 94.72%, respectively. The CaO/CFA catalyst exhibited sustained stability after being reused for up to three cycles.

Patterns of respiratory health services utilization from birth to 5 years of children who experienced adverse birth outcomes

Abstract: Introduction Adverse birth outcomes have important consequences for future lung health. We evaluated patterns of respiratory health services utilization in early childhood among children born preterm (PTB), small and large for gestational age at term (SGA and LGA, respectively), and appropriate-for-gestational age at term. Materials and methods We conducted a population-based retrospective cohort study using administrative health data of all singleton live births in Alberta, Canada between 2005-2010. Data on hospitalizations and emergency department (ED) visits from birth to 5 years were collected for asthma, bronchitis, bronchiolitis, croup, influenza, pneumonia, and other acute upper and lower respiratory tract infections (other URTI and other LRTI, respectively). Adjusted rate ratios were estimated for respiratory ED visits and hospitalizations for adverse birth outcomes using the appropriate-for-gestational age at term group as reference. Age-specific trajectories of total respiratory health services utilization rates for each group were estimated in Poisson models. Results A total of 293,764 episodes of respiratory care from 206,994 children were analyzed. Very PTB children had the highest rates of health services use for all respiratory conditions, particularly for asthma, pneumonia, and bronchiolitis hospitalizations. Moderate/late PTB children also had elevated ED visits and hospitalizations for all respiratory conditions. Children born SGA showed high rates of ED visits for other LRTI, and of hospitalizations for bronchitis, bronchiolitis, and other URTI. Children born LGA had high rates of croup and other URTI ED visits, and of bronchiolitis and bronchiolitis hospitalizations. Age-specific trajectories showed a decreasing trend in the rates of total respiratory health service utilization from birth to five years of age for all groups studied. Children born PTB and LGA at term significantly required more respiratory health services over time compared to the reference group. Conclusion Patterns of paediatric respiratory health services utilization vary according to gestational age and fetal growth.

Chelating agent and substrate effect on hydrothermal growth of Yb3+/Er3+ doped NaYf4 film

Abstract: We report simultaneous crystal growth and deposition of upconverting Yb3+/Er3+ doped NaYF4 film (UCF) on conducting and non-conducting substrates by one-step hydrothermal method. The characteristics such as film topography, morphology, crystallographic phase and upconverting luminescence intensity were found to depend both on the chelating agent and nature of the substrate. The characteristics of the prepared films varied interestingly when either the chelating agent or the substrate was changed. The upconversion emission intensities were found to increase with decreasing film roughness. Further, current investigation demonstrated that the NaYF4 films deposited using EDTA or DTPA chelating agents on ITO substrate and EGTA chelating agent on PG substrate were more uniform and resulted in greater upconverted emission intensities. We envision plausible use of current technology in the development of affordable optical platforms for several optoelectronic applications.

Evaluation of filter, paramagnetic and STAGEtips aided workflows for proteome profiling of Symbiodiniaceae dinoflagellate

Abstract: The integrity of coral reef ecosystems worldwide rests on a fine-tuned symbiotic interaction between an invertebrate and a dinoflagellate microalga from the family Symbiodiniaceae. Recent advances in bottom-up shotgun proteomic approaches and the availability of vast amounts of genetic information about Symbiodiniaceae have provided a unique opportunity to better understand the molecular mechanisms underpinning the interactions of coral-Symbiodiniaceae. However, the resilience of this dinoflagellate cell wall, as well as the presence of polyanionic and phenolics cell wall components, requires the optimization of sample preparation techniques for successful implementation of bottom-up proteomics. Therefore, in this study we compare three different workflows—filter-aided sample preparation (FASP), single-pot solid-phase-enhanced sample preparation (SP3), and stop-and-go-extraction tips (STAGETips, ST)—to develop a high-throughput proteotyping protocol for Symbiodiniaceae algal research. We used the model isolate Symbiodinium tridacnidorum. We show that SP3 outperformed ST and FASP with regard to robustness, digestion efficiency, and contaminant removal, which led to the highest number of total (3799) and unique proteins detected from 23,593 peptides. Most of these proteins were detected with ≥2 unique peptides (73%), zero missed tryptic peptide cleavages (91%), and hydrophilic peptides (>70%). To demonstrate the functionality of this optimized SP3 sample preparation workflow, we examined the proteome of S. tridacnidorum to better understand the molecular mechanism of peridinin-chlorophyll-protein complex (PCP, light harvesting protein) accumulation under low light (LL, 30 μmol photon m−2 s−1). Cells exposed to LL for 7 days upregulated various light harvesting complex (LHCs) proteins through the mevalonate-independent pathway; proteins of this pathway were at 2- to 6-fold higher levels than the control of 120 μmol photon m−2 s−1. Potentially, LHCs which were maintained in an active phosphorylated state by serine/threonine-protein kinase were also upregulated to 10-fold over control. Collectively, our results show that the SP3 method is an efficient high-throughput proteotyping tool for Symbiodiniaceae algal research.

Fuzzy Q-Reinforcement Learning-Based Energy Optimization in IoT Network

Abstract: Motivated by the growing environmental concerned (effect of greenhouse gases) coupled with increasing cost of energy, green computing emerges as promising solution to energy-limited IoT network. As IoT network consists of limited low-battery power smart sensors having ability to connect over wireless network for transmission of data, energy harvested from the environment by the sensor node reduces carbon emission and also recharges its battery continuously, and this harvested energy is used by sensors for its working operation that enhances the lifetime of the IoT network. In this paper, a Fuzzy Q-reinforcement learning (FQRL) scheme using fuzzy logic and model-free Q-learning to optimize the energy consumption in perpetual operations of IoT nodes is presented. The optimization of energy consumption is subject to adaptive duty cycle exercised to smart sensors. The learning agent of FQRL updates If-Then rules of fuzzy controllers according to reward received by learning agent through interacting with environment. The learning agent rewards for good action (increasing the firing strength of rule) and punishes (decrease the firing strength of rule) for bad action subject to maintain the energy neutrality condition. Finally, simulation results show the proposed FQRL outperforms in terms of duty cycle and residual energy after perpetual operation. It means presented algorithm FQRL provides smart sensors to achieve better charging status of their battery and suitable for energy harvested IoT networks.

Fuzzy K-means clustering (FKmC) to maximize the Energy Efficiency in Sensor-Enabled Internet of Things

Abstract: With recent growth of smart devices, wireless green communication is promising solution to improve the network performance, stability and robustness in the Internet of Things (IoT) networks. Clustering is one the best candidate for network partition which enables energy efficient mechanism for data gathering and transmission from sensor enabled IoT nodes, which also improve the quality of service underlying heterogeneous systems in IoT networks. Mostly, researchers proposed clustering approach in the regard of belongings of nodes to cluster or not. In real scenario, nodes has fuzzy relationship with clsuters, in this regard, we present Fuzzy K-means clustering (FKmC) algorithm to form balanced clustering, which divide the network into disjoint clusters focuses on efficient energy consumption in the IoT network. The work proposed soft clustering (FKmC) for better clusters using the membership parameter of sensor nodes and residual energy of sensor nodes. Simulation work is done in three phases; first phase show the effective formation of cluster between proposed FKmc and state-of-art algorithms, Second and third phase of the simulation are performed and found that FKmC outperforms in terms of network lifetime and energy consumption.

ML Based Strategy for Optimal Power Prediction in IoT

Abstract: Internet of Things (IoT) enabled sensors are extensively used for industrial, agriculture, health care and many more application. The primary concern of IoT devices are energy consumption in data gathering and forwarding over Internet due to heterogeneous nature of sensing information. This is because of IoT sensors are often deployed in the monitoring area for several years where human intervention is extremely difficult subject to analyze the environment. In this paper, we predict the energy consumption using various Machine Learning (ML) strategies based on regression model and predict the abnormalities and power usage of inside building or company or inaccessible area. Further, performance is analyzed for heterogeneous resources of data over different version of machine learning algorithm. Furthermore, sophisticated predictive version of machine learning algorithm is selected for different application.

Dissection of function and recognition mechanism of M. tuberculosis ESX-1 secreted virulence factor EspC

Abstract: Mycobacterium tuberculosis uses the ESAT-6 system-1/type VII (ESX-1) system for secretion of virulence proteins into the host cell, however the mechanism of virulence proteins secretion, molecular components and regulation of ESX-1 system are only partly understood. In the current study, we have analyzed the biological function and recognition mechanism between ESX-1 virulence EspC and EccA1 ATPase proteins. The EspC enters into A549 human lung carcinoma cells and exhibited cytotoxicity, as observed in MTT Assay. To understand the recognition mechanism between EspC and EccA1 ATPase, the EspC and EccA1 mutants were generated based on EspC~EccA1 interactions, as observed in molecular modeling. Binding analysis shows that EspC export arm interacts specifically to the {beta}-hairpin insertion motif of the TPR domain of EccA1 ATPase. Mutations in these epitopes lead to significant decrease/or abolish the binding between EspC and EccA1 ATPase. Our study provides insight into biological function and recognition mechanism between EspC and EccA1 ATPase, which can be used as target to prevent EspC secretion/ or in general virulence factor secretion by mycobacterial ESX-1 system.

Effect of Harvesting Unpredictability of resources in Energy Harvesting-WSN

Abstract: Harvested energy in Energy Harvesting-Wireless Sensor Network is never uniform by nature that causes unpredictable energy scavenging by harvesting systems. The energy load of WSN varying in nature and depends on many factors. In order to ensure uninterrupted power supply to the sensors the crucial aspect is to adjust unpredictable energy by using multiple resources. In the proposed study, we bring forth the effect of harvesting energy unpredictability on the network load and proposed a model for minimizing the gap between energy scavenged and energy requirement of the whole architecture.