The SPL gene family (for Squamosa Promoter-binding like Proteins) represents specific transcription factors that have significant roles in abiotic stress tolerance, development and the growth processes of different plants, including initiation of the leaf, branching and development of shoot and fruits. The SPL gene family has been studied in different plant species; however, its role is not yet fully explored in pigeon pea (Cajanus cajan). In the present study, 11 members of the CcSPL gene family were identified in C. cajan. The identified SPLs were classified into nine groups based on a

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

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease with inflammatory nature. One of the leading causes of death in SLE patients is cardiovascular (CVS) morbidity. MiRNA-24 is highly expressed in vascular endothelial cells (VECs). This dysregulated expression pattern is associated with dysfunction or even damage of VECs and leads to the occurrence of cardiovascular diseases. YKL-40 is an inflammatory glycoprotein involved in the pathogenesis of endothelial dysfunction and thereby atherosclerosis. In this work, we aimed at illustrating the possible role of miR-24 and its

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

Polluted water is one of the significant challenges facing the world nowadays, especially with the noticed water shortage recorded in the last period. Different treatment methods, physicochemical and biological, were presented for pollutant removal from polluted wastewater. This review discusses the treatment methods starting from the biological part to help reduction of organics, which are solids that appear in the wastewater. After that, the physicochemical techniques will be discussed as a second part of the treatment process to minimize the heavy metal, dyes, and other pollutants

It is feasible and safe to use unmanned aerial vehicle (UAV) as the data collection platform of the Internet of things (IoT). In order to save the energy loss of the platform and make the UAV perform the collection work effectively, it is necessary to optimize the deployment of UAV. The objective problem is to minimize the sum of the lost energy of UAV and the loss of data transmission of Internet of things devices. The key to solving the problem is to calculate the location of the docking points and the number of docking points when the UAV is working to collect data. This paper proposes a

This paper introduces a heuristic solver based on neural networks and deep learning for the knapsack problem. The solver is inspired by mechanisms and strategies used by both algorithmic solvers and humans. The neural model of the solver is based on introducing several biases in the architecture. We introduce a stored memory of vectors that holds up items representations and their relationship to the capacity of the knapsack and a module that allows the solver to access all the previous outputs it generated. The solver is trained and tested on synthetic datasets that represent a variety of

This article proposes a new problem which is called the Stochastic Travelling Advisor Problem (STAP) in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since the deterministic models are not appropriate for such real-life problems. The

Automatic lane detection is a classical task in autonomous vehicles that traditional computer vision techniques can perform. However, such techniques lack reliability for achieving high accuracy while maintaining adequate time complexity in the context of real-time detection in complex and dynamic road scenes. Deep neural networks have proved their ability to achieve competing accuracy and time complexity while training them on manually labeled data. Yet, the unavailability of segmentation masks for host lanes in harsh road environments hinders fully supervised methods’ operability on such a

This paper presents a modified version of Manta ray foraging optimizer (MRFO) algorithm to deal with global optimization and multilevel image segmentation problems. MRFO is a meta-heuristic technique that simulates the behaviors of manta rays to find the food. MRFO established its ability to find a suitable solution for a variant of optimization problems. However, by analyzing its behaviors during the optimization process, it is observed that its exploitation ability is less than exploration ability, which makes MRFO more sensitive to attractive to a local point. Therefore, we enhanced MRFO by