Plants are sessile beings, so the need of mechanisms to flee from unfavorable circumstances has provided the development of unique and sophisticated responses to environmental stresses. Depending on the degree of plasticity, many morphological, cellular, anatomical, and physiological changes occur in plants in response to abiotic stress. Phytohormones are small molecules that play critical roles in regulating plant growth and development, as well as stress tolerance to promote survival and acclimatize to varying environments. To congregate the challenges of salinity, temperature extremes, and osmotic stress, plants use their genetic mechanism and different adaptive and biological approaches for survival and high production. In the present attempt, we review the potential role of different phytohormones and plant growth-promoting rhizobacteria in abiotic stresses and summarize the research progress in plant responses to abiotic stresses at physiological and molecular levels. We emphasized the regulatory circuits of abscisic acid, indole acetic acid, cytokinins, gibberellic acid, salicylic acid, brassinosteroids, jasmonates, ethylene, and triazole on exposure to abiotic stresses. Current progress is exemplified by the identification and validation of several significant genes that enhanced crop tolerance to stress in the field. These findings will make the modification of hormone biosynthetic pathways for the transgenic plant generation with augmented abiotic stress tolerance and boosting crop productivity in the coming decades possible.

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Infectious diseases and cancers are some of the commonest causes of deaths throughout the world. The previous two decades have witnessed a combined endeavor across various biological sciences to address this issue in novel ways. The advent of recombinant DNA technologies has provided the tools for producing recombinant proteins that can be used as therapeutic agents. A number of expression systems have been developed for the production of pharmaceutical products. Recently, advances have been made using plants as bioreactors to produce therapeutic proteins directed against infectious diseases and cancers. This review highlights the recent progress in therapeutic protein expression in plants (stable and transient), the factors affecting heterologous protein expression, vector systems and recent developments in existing technologies and steps towards the industrial production of plant-made vaccines, antibodies, and biopharmaceuticals.

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Quilliam R (2016) Integration of biochar with animal manure and nitrogen for improving maize yields and soil properties in calcareous semiarid agroecosystems, Field Crops Research, 195,

Uplink and Downlink channel estimation in massive Multiple Input Multiple Output (MIMO) systems is an intricate issue because of the increasing channel matrix dimensions. The channel feedback overhead using traditional codebook schemes is very large, which consumes more bandwidth and decreases the overall system efficiency. The purpose of this paper is to decrease the channel estimation overhead by taking the advantage of sparse attributes and also to optimize the Energy Efficiency (EE) of the system. To cope with this issue, we propose a novel approach by using Compressed-Sensing (CS), Block Iterative-Support-Detection (Block-ISD), Angle-of-Departure (AoD) and Structured Compressive Sampling Matching Pursuit (S-CoSaMP) algorithms to reduce the channel estimation overhead and compare them with the traditional algorithms. The CS uses temporal-correlation of time-varying channels to produce Differential-Channel Impulse Response (DCIR) among two CIRs that are adjacent in time-slots. DCIR has greater sparsity than the conventional CIRs as it can be easily compressed. The Block-ISD uses spatial-correlation of the channels to obtain the block-sparsity which results in lower pilot-overhead. AoD quantizes the channels whose path-AoDs variation is slower than path-gains and such information is utilized for reducing the overhead. S-CoSaMP deploys structured-sparsity to obtain reliable Channel-State-Information (CSI). MATLAB simulation results show that the proposed CS based algorithms reduce the feedback and pilot-overhead by a significant percentage and also improve the system capacity as compared with the traditional algorithms. Moreover, the EE level increases with increasing Base Station (BS) density, UE density and lowering hardware impairments level.

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The present review provides information relevant to issues and challenges in MEMS testing techniques that are implemented to analyze the microelectromechanical systems (MEMS) behavior for specific application and operating conditions. MEMS devices are more complex and extremely diverse due to the immersion of multidomains. Their failure modes are distinctive under different circumstances. Therefore, testing of these systems at device level as well as at mass production level, that is, parallel testing, is becoming very challenging as compared to the IC test, because MEMS respond to electrical, physical, chemical, and optical stimuli. Currently, test systems developed for MEMS devices have to be customized due to their nondeterministic behavior and complexity. The accurate measurement of test systems for MEMS is difficult to quantify in the production phase. The complexity of the device to be tested required maturity in the test technique which increases the cost of test development; this practice is directly imposed on the device cost. This factor causes a delay in time-to-market.