Comparative transcriptome analysis reveals transcriptional regulation of anthocyanin biosynthesis in purple radish (Raphanus sativus L.).

Radish exhibits significant variation in color, particularly in sprouts, leaves, petals, fleshy roots, and other tissues, displaying a range of hues such as green, white, red, purple, and black. Although extensive research has been conducted on the color variation of radish, the underlying mechanism behind the variation in radish flower color remains unclear. To date, there is a lack of comprehensive research investigating the variation mechanism of radish sprouts, leaves, fleshy roots, and flower organs. This study aims to address this gap by utilizing transcriptome sequencing to acquire transcriptome data for white and purple radish flowers. Additionally, the published transcriptome data of sprouts, leaves, and fleshy roots were incorporated to conduct a systematic analysis of the regulatory mechanisms underlying anthocyanin biosynthesis in these four radish tissues. The comparative transcriptome analysis revealed differential expression of the anthocyanin biosynthetic pathway genes DFR, UGT78D2, TT12 and CPC in the four radish tissues. Additionally, the WGCNA results identified RsDFR.9c and RsUGT78D2.2c as hub genes responsible for regulating anthocyanin biosynthesis. By integrating the findings from the comparative transcriptome analysis, WGCNA, and anthocyanin biosynthetic pathway-related gene expression patterns, it is hypothesized that genes RsDFR.9c and RsUGT78D2.2c may serve as pivotal regulators of anthocyanins in the four radish tissues. Furthermore, the tissue-specific expression of the four copies of RsPAP1 is deemed crucial in governing anthocyanin synthesis and accumulation. Our results provide new insights into the molecular mechanism of anthocyanin biosynthesis and accumulation in different tissues of radish.

Gd(III)-Catalyzed Regio-, Diastereo-, and Enantioselective [4 + 2] Photocycloaddition of Naphthalene Derivatives.

Catalytic asymmetric dearomatization (CADA) reactions have evolved into an efficient strategy for accessing chiral polycyclic and spirocyclic scaffolds from readily available planar aromatics. Despite the significant developments, the CADA reaction of naphthalenes remains underdeveloped. Herein, we report a Gd(III)-catalyzed asymmetric dearomatization reaction of naphthalene with a chiral PyBox ligand via visible-light-enabled [4 + 2] cycloaddition. This reaction features application of a chiral Gd/PyBox complex, which regulates the reactivity and selectivity simultaneously, in excited-state catalysis. A wide range of functional groups is compatible with this protocol, giving the highly enantioenriched bridged polycycles in excellent yields (up to 96%) and selectivity (up to >20:1 chemoselectivity, >20:1 dr, >99% ee). The synthetic utility is demonstrated by a 2 mmol scale reaction, removal of directing group, and diversifications of products. Preliminary mechanistic experiments are performed to elucidate the reaction mechanism.

Molecular evolution analysis of MYB5 in Brassicaceae with specific focus on seed coat color of Brassica napus.

BACKGROUND: MYB transcription factors are splay a vital role in plant biology, with previous research highlighting the significant impact of the R2R3-MYB-like transcription factor MYB5 on seed mucilage biosynthesis, trichome branching, and seed coat development. However, there is a dearth of studies investigating its role in the regulation of proanthocyanidin (PA) biosynthesis. RESULTS: In this study, a total of 51 MYB5 homologous genes were identified across 31 species belonging to the Brassicaceae family, with particular emphasis on Brassica napus for subsequent investigation. Through phylogenetic analysis, these genes were categorized into four distinct subclasses. Protein sequence similarity and identity analysis demonstrated a high degree of conservation of MYB5 among species within the Brassicaceae family. Additionally, the examination of selection pressure revealed that MYB5 predominantly underwent purifying selection during its evolutionary history, as indicated by the Ka/Ks values of all MYB5 homologous gene pairs being less than one. Notably, we observed a higher rate of non-synonymous mutations in orthologous genes compared to paralogous genes, and the Ka/Ks value displayed a stronger correlation with Ka. In B. napus, an examination of expression patterns in five tissues revealed that MYB5 exhibited particularly high expression in the black seed coat. The findings from the WGCNA demonstrated a robust correlation between MYB5 and BAN(ANR) associated with PA biosynthesis in the black seed coat, providing further evidence of their close association and co-expression. Furthermore, the results obtained from of the analysis of protein interaction networks offer supplementary support for the proposition that MYB5 possesses the capability to interact with transcriptional regulatory proteins, specifically TT8 and TT2, alongside catalytic enzymes implicated in the synthesis of PAs, thereby making a contribution to the biosynthesis of PAs. These findings imply a plausible and significant correlation between the nuique expression pattern of MYB5 and the pigmentation of rapeseed coats. Nevertheless, additional research endeavors are imperative to authenticate and substantiate these findings. CONCLUSIONS: This study offers valuable insights into the genetic evolution of Brassicaceae plants, thereby serving as a significant reference for the genetic enhancement of Brassicaceae seed coat color.

A Magnetic Beads-Based Sandwich Chemiluminescence Enzyme Immunoassay for the Rapid and Automatic Detection of Lactoferrin in Milk.

Lactoferrin (LF), an iron-binding glycoprotein with immunological properties and a high nutritional value, has emerged as a prominent research focus in the field of food nutrition. Lactoferrin is widely distributed in raw milk and milk that has undergone low-temperature heat treatment during pasteurization, making its rapid and accurate detection crucial for ensuring the quality control of dairy products. An enzyme-linked immunosorbent assay-based analytical protocol has often been referred to for the detection of LF in real samples. Signal amplification was accomplished using the streptavidin-biotin system. Here, an automated magnetic beads-based sandwich chemiluminescence enzyme immunoassay (MBs-sCLEIA) system was developed for the quantification of lactoferrin in pasteurized milk. The MBs-sCLEIA system consists of an automated chemiluminescence-based analyzer and a lactoferrin MBs-sCLEIA assay kit. Notably, our proposed method eliminates the need for pretreatment procedures and enables the direct addition of milk samples, allowing for the automatic quantitative detection of lactoferrin within a rapid 17 min timeframe for up to eight samples simultaneously. The MBs-sCLEIA was linear over the range of 7.24-800 ng/mL and displayed a limit of detection (LOD) of 2.85 ng/mL. As its good recovery and CV values indicate, the method exhibited high precision and accuracy. Furthermore, it was verified that it was selective towards five additional common milk proteins. A good correlation was observed between the results from the MBs-sCLEIA and heparin affinity column-HPLC (r2 = 0.99042), which proves to be a useful and practicable way of conducting an accurate analysis of lactoferrin in dairy products.

Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China.

This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.

A toe-inspired rigid-flexible coupling wheel design method for improving the terrain adaptability of a sewer robot.

The human toe, characterized by its rigid-flexible structure comprising hard bones and flexible joints, facilitates adaptive and stable movement across varied terrains. In this paper, we utilized a motion capture system to study the adaptive adjustments of toe joints when encountering obstacles. Inspired by the mechanics of toe joints, we proposed a novel design method for a rigid-flexible coupled wheel. The wheel comprises multiple elements: a rigid skeleton, supporting toes, connecting shafts, torsion springs, soft tendons, and damping pads. The torsion springs connect the rigid frame to the supporting toes, enabling them to adapt to uneven terrains and pipes with different diameters. The design was validated through kinematic and dynamic modeling, rigid-flexible coupled dynamics simulation, and stress analysis. Different stiffness coefficients of torsion springs were compared for optimal wheel design. Then, the wheel was applied to a sewer robot, and its performance was evaluated and compared with a pneumatic rubber tire in various experiments, including movement on flat surfaces, overcoming small obstacles, adaptability tests in different terrains, and active driving force tests in dry and wet pipelines. The results prove that the designed wheel showed better stability and anti-slip properties than conventional tires, making it suitable for diverse applications such as pipeline robots, desert vehicles, and lunar rovers.

Chromosome-scale assembly of the wild cereal relative Elymus sibiricus.

Elymus species, belonging to Triticeae tribe, is a tertiary gene pool for improvement of major cereal crops. Elymus sibiricus, a tetraploid with StH genome, is a typical species in the genus Elymus, which is widely utilized as a high-quality perennial forage grass in template regions. In this study, we report the construction of a chromosome-scale reference assembly of E. sibiricus line Gaomu No. 1 based on PacBio HiFi reads and chromosome conformation capture. Subgenome St and H were well phased by assisting with kmer and subgenome-specific repetitive sequence. The total assembly size was 6.929 Gb with a contig N50 of 49.518 Mb. In total, 89,800 protein-coding genes were predicted. The repetitive sequences accounted for 82.49% of the genome in E. sibiricus. Comparative genome analysis confirmed a major species-specific 4H/6H reciprocal translocation in E. sibiricus. The E. sibiricus assembly will be much helpful to exploit genetic resource of StH species in genus Elymus, and provides an important tool for E. sibiricus domestication.

One-pot green synthesis of ZIF-8/IgG composite for the precise orientation and protection of antibody and its application in purification and detection of aflatoxins in peanut oil.

This study presents a novel approach toward the one-pot green synthesis of ZIF-8/IgG composite, focusing on its precise orientation and protection of the anti-aflatoxins antibody. The antibody orientation is achieved through the specific binding of IgG to the Fc region of the antibody, while the antibody protection is accomplished by the structural change restriction of ZIF-8 framework to the antibody. Consequently, the antibody exhibits enhanced target capability and significantly improved tolerance to organic solvents. The ZIF-8/IgG/anti-AFT was employed for the purification and detection of AFTs by coupling with UPLC. Under optimized conditions, the recoveries of spiked AFTs in peanut oils are between 86.1% and 106.4%, with relative standard deviations (RSDs) ranging from 0.8% to 8.8%. The linearity range is 0.5-20.0 ng for AFB1 and AFG1, 0.125-5.0 ng for AFB2 and AFG2, the limit of detection is 0.1 ng for AFB1 and AFG1, 0.03 ng for AFB2 and AFG2.