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Present study was carried out during rainy (kharif) seasons of 2020–22 at ICAR-Indian Agricultural Research Institute, New Delhi, to examine transcription factors (TFs) in drought tolerance using two rice (Oryza sativa L.) cultivars, drought-tolerant Nagina-22 and drought-sensitive IR-64. Immature panicles were used to prepare transcriptome libraries, sequenced with Paired-End 150 bp chemistry for both control and drought-treated samples. Bioinformatic analysis of the data using TFs extracted from PlantTF database revealed that majority (335) of the TFs were up-regulated in Nagina-22, while 92 were down-regulated under stress. Conversely, a significant number of TFs (359) were down-regulated in IR-64 under stress. Gene ontology and gene regulatory network analyses of the differentially expressed TFs indicated that several crucial biological processes and molecular functions were enriched with higher number of up-regulated TFs in Nagina-22 under stress. Significantly up-regulated expression of TFs, particularly WRKY, ERF, MYB, NAC, and bHLH, in panicle of N-22 must be responsible for terminal drought tolerance. Our findings confirm the master-regulator role of TFs in gene-regulatory networks essential for tolerance to complex trait like drought. The information might be valuable in selecting potential candidate parental lines for drought tolerance breeding and genetic manipulation of rice cultivars towards the development of climate-resilient cultivars.

AbstractThis study examines the influence of sodium hydroxide treatment on the tensile and flexural properties of carbon/ramie/epoxy hybrid composites, validated through artificial neural network modeling. Among untreated, 1 wt.%, and 5 wt.% sodium hydroxide-treated samples, the 3 wt.% treatment showed the highest improvements, achieving a tensile strength of 524 MPa and a flexural strength of 697 MPa, with respective increases of 27.48% and 43.75%. 1 wt.%, 3 wt.%, and 5 wt.% of sodium hydroxide degraded the ramie fibers, causing weight losses of 4.6%, 8.5%, and 18.5%, respectively. Fourier transform infrared spectroscopy confirmed the removal of surface impurities such as wax, hemicellulose, and lignin, enhancing the bonding between the fibers and the matrix, while field emission scanning electron microscopy revealed improved structural integrity. The superior properties of the 3 wt.% sodium hydroxide-treated composite were attributed to a stronger interface between the fibers and the matrix. Additionally, the artificial neural network model accurately predicted the tensile and flexural strengths using time, load, and extension as inputs, with regression coefficients of 0.99998 and 0.99982 and a minimal error of 3.29%. These results highlight the optimal performance of 3 wt.% sodium hydroxide-treated composites as sustainable materials for structural applications in the automotive, aerospace, and construction sectors. [ABSTRACT FROM AUTHOR]

Many automobile components and machine parts can be fabricated using the Fused Deposition Modeling (FDM) process with materials such as Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate Glycol (PET-G), and polymeric composite materials (e.g., PLA with carbon fiber, PLA with glass fiber). In this study, a new polymeric composite material was fabricated using Polylactic Acid and natural flax fiber was analysed for tensile stress, elongation, and impact load resistance using Taguchi Analysis. This analysis optimized the printing parameters, including layer thickness (0.15, 0.25, 0.35 mm), nozzle movement speed (80, 100, 120 mm s ^−1 ), filling structure (Lines - a, Triangular - b, and Octet - c), and occupancy rate (20%, 40%, 60%). The American Society for Testing and Materials (ASTM) standards for tensile strength (ASTM D638) and impact strength (ASTM D256) were used for evaluation. As a result, layer thickness was found to be the most effective variable for improving tensile characteristics, compared to extruder temperature, occupancy rate, or filling structure pattern. Mechanical properties including a layer thickness of 0.25 mm, an occupancy ratio of 20% for the bottom of the 2nd layer and 40% for the top of the 4th layer, triangular and octet filling structures, a nozzle speed of 100 mm s ^−1 , and an extruder temperature of 200 °C are considered the most appropriate parameters for producing automotive parts in Three Dimensional (3D) Printing. Due to its tensile properties and impact strength resistance, these settings can be utilized in potential application in a wide variety of machine parts and vehicle components.

Sahana Ramanathan,1 Govindaraju Archunan,2 Muthusamy Sivakumar,3 Subramanian Tamil Selvan,4 A Lenin Fred,5 Sundramurthy Kumar,1 Balázs Gulyás,1 Parasuraman Padmanabhan1 1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; 2Department of Animal Science, Centre for Pheromone Technology (CPT), Bharathidasan University, Tiruchirappalli, India; 3Nanoscience and Technology, Anna University – BIT Campus, Tiruchirappalli, India; 4Institute of Materials Research and Engineering, Singapore; 5Mar Ephraem College of Engineering and Technology, Kanyakumari, India Abstract: The preeminent treatments for neurodegenerative disease are often unavailable due to the poor accessibility of therapeutic drugs. Moreover, the blood–brain barrier (BBB) effectively blocks the transfer of cells, particles and large molecules, ie, drugs, across the brain. The most important challenge in the treatment of neurodegenerative diseases is the development of targeted drug delivery system. Theranostic strategies are known to combine therapeutic and diagnostic capabilities together. The aim of this review was to record the response to treatment and thereby improve drug safety. Nanotechnology offers a platform for designing and developing theranostic agents that can be used as an efficient nano-carrier system. This is achieved by the manipulation of some of the properties of nanoparticles (NPs), thereby enabling the attachment of suitable drugs onto their surface. The results provide revolutionary treatments by stimulation and thus interaction with targeted sites to promote physiological response with minimum side effects. This review is a brief discussion of the administration of drugs across the brain and the advantages of using NPs as an effective theranostic platform in the treatment of Alzheimer’s, Parkinson’s, epilepsy and Huntington’s disease. Keywords: theranostics, neurological disease, nanoparticles, imaging

Ground motion prediction equations (GMPEs) play a key role in seismic hazard assessment (SHA). Considering the seismo-tectonic, geophysical, and geotectonic characteristics of a target region, all the GMPEs may not be suitable in predicting the observed ground motion effectively. With a fairly large number of published GMPEs, the selection and ranking of suitable GMPEs for the design of logic trees in SHA for a particular target region have become a necessity of late. This paper presents a detailed quantitative evaluation of performance of 16 GMPEs against recorded ground motion data in two target regions, characterized by distinct seismo-tectonic, geophysical, and geotectonical nature. The dataset comprises 465 three-component spectral accelerograms corresponding to 122 earthquake events. The suitability of a GMPE is tested by two widely accepted data-driven statistical methods, namely, likelihood (LH) and log-likelihood (LLH) method. Different suites of GMPEs are shown suitable for different periods of interest. The results will be useful to scientists and engineers for microzonation and estimation of seismic design parameters for the design of earthquake-resistant structures in these regions. [ABSTRACT FROM AUTHOR]

Predicting the axial Shortening strength of concrete-filled steel tubular (CFST) columns is an important problem that this study attempts to solve for civil engineering projects. We suggest using a deep learning-based artificial neural network (ANN) model to address this issue, taking into account the intricate relationship between steel tube and core concrete. The model, called ANN-SFRC (Steel Fibre Reinforced Concrete), surpasses an R2 threshold of 0.90 and achieves impressive R2 values across different types of CFST columns. Compared to traditional linear regression methods, the ANN-SFRC model significantly improves accuracy, with an observed inaccuracy of less than 3% compared to actual values. With its reliable approach to forecasting the behavior of CFST columns under axial compression, this high-performance instrument enhances safety and accuracy during the design and planning stages of civil engineering. [ABSTRACT FROM AUTHOR]