Capsaicin is the primary capsaicinoid compound responsible for the spiciness of chilli peppers. Several known and unknown genes synthesize capsaicin through various metabolic pathways, such as the phenylpropanoid or the L-valine metabolism pathways. We conducted comprehensive comparative genomics and proteomics analyses to identify genes and proteins associated with the capsaicin pathway in Capsicum chinense, Capsicum baccatum and the two C.annuum cultivars, CM334 and ECW. A BLAST search against the NCBI database identified 26 and 58 enzyme genes and proteins, respectively. These enzyme genes

The MYB transcription factor (TF) are among the largest gene families of plants being responsible for several biological processes. The R2R3-MYB gene family are integral player regulating plant primary and secondary metabolism, growth and development, and responses to hormones and stresses. The phylogenetic analysis combined with gene structure analysis and motif determination resulted in division of R2R3-MYB gene family into 27 subgroups. Evidence generated from synteny analyses indicated that CqR2R3-MYBs gene family is featured by tandem and segmental duplication events. On the basis of RNA

Chickpea (Cicer arietinum) is a grain crop that is an important source of protein, vitamins, carbohydrates and minerals. It is highly sensitive to salt stress, and salt damage to cellular homeostasis and protein folding affects production. Plants have several mechanisms to prevent cellular damages under abiotic stresses, such as proteins in the endoplasmic reticulum (protein isulfide somerases (PDIs) and PDI-like proteins), which help prevent the build-up of mis-folded proteins that are damaged under abiotic stresses. In this study, we completed initial comprehensive genome-wide analysis of

【Objective】 The exploration of germplasm and gene resources in rice for high nitrogen efficiency, along with the elucidation of their molecular mechanisms and genetic effects, represents a significant focus and goal within current research efforts on rice nitrogen use efficiency (NUE).【Method】 To identify the variant loci and candidate genes associated with rice NUE, we collected 190 Asian rice accessions as an association population. After thorough filtering and screening, we obtained 3, 934, 195 high-quality single nucleotide polymorphisms (SNPs). Under field conditions, two nitrogen

Direct detection of biotinylated proteins (DiDBiT) is a proteomic method that can enrich and detect newly synthesized proteins (NSPs) labeled with bio-orthogonal amino acids with 20-fold improved detectability compared to conventional methods. However, DiDBiT has currently been used to compare only two conditions per experiment. Here, we present DiDBiT-TMT, a method that can be used to quantify NSPs across many conditions and replicates in the same experiment by combining isobaric tandem mass tagging (TMT) with DiDBiT. We applied DiDBiT-TMT to brain slices to determine changes in the de novo

[No abstract available]

Face shape classification is considered one of the trending topics in the artificial intelligence research field. Face shape classification can be employed in many broad-scoped projects, such as hairstyle recommendation systems in the beauty and fashion industry. In this paper, the inception v3 model was employed to reach the highest possible performance for classifying the different face shapes. The model was re-trained after applying a proposed sequence of preprocessing techniques, including image straightening, cropping, resizing, and normalization. The model was re-trained on different

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas.Machine learning (ML)model is recently applied to predict antenna parameters.ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a

Machine learning (ML) and deep learning (DL) have been broadly used in our daily lives in different ways. Early detection of COVID-19 built on chest Computerized tomography CT empowers suitable management of patients and helps control the spread of the disease. We projected an artificial intelligence (AI) system for rapid COVID-19 detection using analysis of CTs of COVID-19 depending on the AI system. We developed and evaluated our system on a large dataset with more than 3000 CT volumes from COVID-19, viral community-acquired pneumonia (CAP) and non-pneumonia subjects—1601 positive cases

Random numbers play an essential role in guaranteeing secrecy in most cryptographic systems. A chaotic optical signal is exploited to achieve high-speed random numbers. It could be generated by using one or more semiconductor lasers with external optical feedback. However, this system faces two major issues, high peak to average power ratio (PAPR) and parameter variations. These issues highly affected the randomness of the generated bitstreams. In this paper, we use a non-linear compression technique to compand the generated signal before it is quantized to avoid the effects of the PAPR. Also