Phylogenomic curation of Ovate Family Proteins (OFPs) in the U's Triangle of Brassica L. indicates stress-induced growth modulation.

The Ovate Family Proteins (OFPs) gene family houses a class of proteins that are involved in regulating plant growth and development. To date, there is no report of the simultaneous functional characterization of this gene family in all members of U's Triangle of Brassica. Here, we retrieved a combined total of 256 OFP protein sequences and analyzed their chromosomal localization, gene structure, conserved protein motif domains, and the pattern of cis-acting regulatory elements. The abundance of light-responsive elements like G-box, MRE, and GT1 motif suggests that OFPs are sensitive to the stimuli of light. The protein-protein interaction network analysis revealed that OFP05 and its orthologous genes were involved in regulating the process of transcriptional repression through their interaction with homeodomain transcription factors like KNAT and BLH. The presence of domains like DNA binding 2 and its superfamily speculated the involvement of OFPs in regulating gene expression. The biotic and abiotic stress, and the tissue-specific expression analysis of the RNA-seq datasets revealed that some of the genes such as BjuOFP30, and BnaOFP27, BolOFP11, and BolOFP10 were highly upregulated in seed coat at the mature stage and roots under various chemical stress conditions respectively which suggests their crucial role in plant growth and development processes. Experimental validation of prominent BnaOFPs such as BnaOFP27 confirmed their involvement in regulating gene expression under salinity, heavy metal, drought, heat, and cold stress. The GO and KEGG pathway enrichment analysis also sheds light on the involvement of OFPs in regulating plant growth and development. These findings have the potential to serve as a forerunner for future studies in terms of functionally diverse analysis of the OFP gene family in Brassica and other plant species.

Robust and adaptive terrain classification and gait event detection system.

Real-time gait event detection (GED) system can be utilized for gait analysis and tracking fitness activities. GED for various types of terrains (e.g., stair-walk, uneven surfaces, etc.) is still an open research problem. This study presents an inertial sensor-based approach for real-time GED system that works for diverse terrains in an uncontrolled environment. The GED system classifies three types of terrains, i.e., flat-walk, stair-ascend and stair-descend, with an average classification accuracy of 99%. It also accurately detects various gait events, including, toe-strike, heel-rise, toe-off, and heel-strike. It is computationally efficient, implemented on a low-cost microcontroller, works in real-time and can be used in portable rehabilitation devices for use in dynamic environments.

De novo transcriptome assembly of Dalbergia sissoo Roxb. (Fabaceae) under Botryodiplodia theobromae-induced dieback disease.

Dalbergia sissoo Roxb. (Shisham) is a timber-producing species of economic, cultural, and medicinal importance in the Indian subcontinent. In the past few decades, Shisham's dieback disease caused by the fungus Botryodiplodia theobromae has become an evolving issue in the subcontinent endangering its survival. To gain insights into this issue, a standard transcriptome assembly was deployed to assess the response of D. sissoo at the transcriptomic level under the stress of B. theobromae infection. For RNA isolation, the control and infected leaf tissue samples were taken from 1-year-old greenhouse-grown D. sissoo plants after 20 days of stem-base spore inoculation. cDNA synthesis was performed from these freshly isolated RNA samples that were then sent for sequencing. About 18.14 Gb (Giga base) of data was generated using the BGISEQ-500 sequencing platform. In terms of Unigenes, 513,821 were identified after a combined assembly of all samples and then filtering the abundance. The total length of Unigenes, their average length, N50, and GC-content were 310,523,693 bp, 604 bp, 1,101 bp, and 39.95% respectively. The Unigenes were annotated using 7 functional databases i.e., 200,355 (NR: 38.99%), 164,973 (NT: 32.11%), 123,733 (Swissprot: 24.08%), 142,580 (KOG: 27.75%), 139,588 (KEGG: 27.17%), 99,752 (GO: 19.41%), and 137,281 (InterPro: 26.72%). Furthermore, the Transdecoder detected 115,762 CDS. In terms of SSR (Simple Sequence Repeat) markers, 62,863 of them were distributed on 51,508 Unigenes and on the predicted 4673 TF (Transcription Factor) coding Unigenes. A total of 16,018 up- and 19,530 down-regulated Differentially Expressed Genes (DEGs) were also identified. Moreover, the Plant Resistance Genes (PRGs) had a count of 9230. We are hopeful that in the future, these identified Unigenes, SSR markers, DEGs and PRGs will provide the prerequisites for managing Shisham dieback disease, its breeding, and in tree improvement programs.

A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.

Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future.

Depolymerization of lignin: Recent progress towards value-added chemicals and biohydrogen production.

The need for alternative sources of energy became increasingly urgent as demand for energy and the use of fossil fuels both soared. When processed into aromatic compounds, lignin can be utilized as an alternative to fossil fuels, however, lignin's complex structure and recalcitrance make depolymerization impractical. This article presented an overview of the most recent advances in lignin conversion, including process technology, catalyst advancement, and case study-based end products. In addition to the three established methods (thermochemical, biochemical, and catalytic depolymerization), a lignin-first strategy was presented. Depolymerizing different forms of lignin into smaller phenolic molecules has been suggested using homogeneous and heterogeneous catalysts for oxidation or reduction. Limitations and future prospects of lignin depolymerization have been discussed which suggests that solar-driven catalytic depolymerization through photocatalysts including quantum dots offers a unique pathway to obtain the highly catalytic conversion of lignin.

Triggering photo fermentative biohydrogen production through NiFe2O4 photo nanocatalysts with various excitation sources.

The triggering effects of nickel ferrite (NiFe2O4) photo nanocatalysts on photo fermentative hydrogen production (PFHP), and metabolic pathways under various excitation sources (incandescent lamp, Xenon lamp, and 532 laser) have been investigated. Compare to the control group (CG) highest cumulative hydrogen volume (CHV) and the maximum hydrogen production rate (HPR) of 568.8 mL and 9.17 mL/h, respectively were achieved at a loading centration of 150 mg/L excited with an incandescent lamp. The change in metabolites with NiFe2O4 incorporation suggests that bacterial activity is significantly affected by photo nanocatalysts. Triggering of NiFe2O4 by laser excitation showed the highest HPR of 7.83 mL /h within 24 h, which greatly reduces the lag time. The microbial community investigation showed that the addition of NiFe2O4 photo nanocatalysts and the change of light source effectively improved the microbial community structure and increased the abundance of hydrogen-producing bacteria (HPB) which leads to enhanced hydrogen production.

Advances in the catalyzed photo-fermentative biohydrogen production through photo nanocatalysts with the potential of selectivity, and customization.

Photo nanocatalyst have shownpromise in a variety of fields, including biohydrogen production where their catalytic efficiency is related to size, surface-to-volume ratio, and increasing the number of atoms on the surface. They can harvest solar light to create electron-hole pairs which is the key mechanism to define its catalytic efficiency, thus requiring suitable excitation wavelength, band energy, and crystal imperfections. In this review, a discussion on the role of photo nanocatalysts to catalyze biohydrogen production has been carried out. Photo nanocatalysts feature a large bandgap, andhigh defect concentration, thus having the ability to be tuned for their characteristics. Customization of the photo nanocatalyst has been addressed. Mechanism of the photo nanocatalysts in catalyzing biohydrogen has been discussed. Limiting factors of photo nanocatalysts were highlighted and several recommendations have been made to enhance the effective utilization of these photo nanocatalysts to enhance photo-fermentative biohydrogen production from biomass wastes.

26.48% efficient and stable FAPbI3 perovskite solar cells employing SrCu2O2 as hole transport layer.

In general, formamidinium lead tri-iodide (FAPbI3) based perovskite solar cells are more stable than their methylammonium lead tri-iodide (MAPbI3) counterparts. However, when it comes to power conversion efficiency (PCE), MAPbI3 solar cells are far better. This work aimed to enhance the power conversion efficiency of FAPbI3 solar cells without compromising their thermal stability. The numerical analysis of 6 different proposed structures with 2 carbon based electron transport materials (C60, PCBM) and 3 copper based hole transport materials (SrCu2O2, CuSCN, CuSbS2) is performed using SCAPS-1D software. The parameters are used from various theoretical and experimental published works. In order to investigate the performance of each proposed structure, the defect density, layer thickness and doping concentration of the absorber layer, electron transport layer (ETL) and hole transport layer (HTL) are varied, and optimized parameters are enumerated. The best simulation result having PCE of 26.48% is achieved with 1.25 V open circuit voltage (V OC), 23.51 mA cm-2 short circuit current (J SC) and 89.5% fill factor (FF) for FTO/PCBM/FAPbI3/SrCu2O2/Au. The proposed structure also showed good thermal stability at 300 K. Moreover, the effects of the different charge transport layer on the energy band alignment, electric field, recombination and IV characteristics are also investigated in detail.

A Study on the Mechanical Characteristics of Glass and Nylon Fiber Reinforced Peach Shell Lightweight Concrete.

In the current study, the utilization of glass and nylon fibers in various percentages are added to enhance the mechanical performance of peach shell lightweight concrete. Glass and nylon fibers were added at 2%, 4%, 6%, and 8% by cement weight. The results showed that, as we added the glass and nylon fibers, the density of peach shell concrete was reduced by 6.6%, and the compressive, split tensile and flexural strength were enhanced by 10.20%, 60.1%, and 63.49%. The highest strength that was obtained in compressive, split tensile, and flexural strength at 56 days was 29.4 MPa, 5.2 MPa, and 6.3 MPa, respectively, with 6% of glass fiber in peach shell concrete. Mechanical test results showed that post-failure toughness and modulus of elasticity of peach shell concrete is enhanced with the utilization of fibers. To verify our lab results, a statistical analysis, such as response surface methodology, was performed to make a statistical model, it was confirmed by both lab results and statistical analysis that the mechanical performance of peach shell concrete could be significantly improved by adding glass fibers as compared to nylon fibers. With the use of fibers, the water absorption and porosity were slightly increased. Hence, the glass and nylon fibers can be used to improve the peach shell concrete mechanical properties to make concrete eco-friendly, sustainable, and lightweight.

Classification of forearm EMG signals for 10 motions using optimum feature-channel combinations.

Electromyography (EMG) is the study of electrical activity in the muscles. We classify EMG signals from surface electrodes (channels) using Artificial Neural Network (ANN). We evaluate classification performance of 10 different hand motions using several feature-channel combinations with wrapper method. Highest classification accuracy of 98.7% is achieved with each feature-channel combination. Compared to previous studies, we achieve the highest accuracy for 10 classes with lower number of feature-channel combination. We reduce ANN complexity without compromising the classification accuracy for deployment in low-end hardware with limited computational power along with improving the design of a low-cost hardware for EMG signal acquisition.