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As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots.

SCARF1 (scavenger receptor class F member 1, SREC-1 or SR-F1) is a type I transmembrane protein that recognizes multiple endogenous and exogenous ligands such as modified low-density lipoproteins (LDLs) and is important for maintaining homeostasis and immunity. But the structural information and the mechanisms of ligand recognition of SCARF1 are largely unavailable. Here, we solve the crystal structures of the N-terminal fragments of human SCARF1, which show that SCARF1 forms homodimers and its epidermal growth factor (EGF)-like domains adopt a long-curved conformation. Then, we examine the interactions of SCARF1 with lipoproteins and are able to identify a region on SCARF1 for recognizing modified LDLs. The mutagenesis data show that the positively charged residues in the region are crucial for the interaction of SCARF1 with modified LDLs, which is confirmed by making chimeric molecules of SCARF1 and SCARF2. In addition, teichoic acids, a cell wall polymer expressed on the surface of gram-positive bacteria, are able to inhibit the interactions of modified LDLs with SCARF1, suggesting the ligand binding sites of SCARF1 might be shared for some of its scavenging targets. Overall, these results provide mechanistic insights into SCARF1 and its interactions with the ligands, which are important for understanding its physiological roles in homeostasis and the related diseases.

Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

Background Argonaute (AGO), Dicer-like (DCL), and RNA-dependent RNA polymerase (RDR) are essential components of RNA silencing pathways in plants. These components are crucial for the generation and regulatory functions of small RNAs, especially in plant development and response to environmental stresses. Despite their well-characterized functions in other plant species, there is limited information about these genes and their stress responses in centipedegrass ( Eremochloa ophiuroides ), a key turfgrass species. Results Using genome-wide analysis we identified 20 AGO , 6 DCL , and 10 RDR members in centipedegrass and provided a comprehensive overview of their characteristics. We performed the chromosomal location, gene duplication, syntenic analysis, conserve motif, gene structure, and cis -acting elements analysis. And conducted phylogenetic analyses to clarify the evolutionary relationships among the EoAGO , EoDCL , and EoRDR gene families. Three-dimensional modeling prediction of EoAGO, EoDCL, and EoRDR proteins supported the phylogenetic classification. Furthermore, we examined the expression patterns of these genes in different tissues (spike, stem, leaf, root, and flower) and under different stress conditions (cold, salt, drought, aluminum, and herbicide) using RT-qPCR. The results revealed that most of EoAGO , EoDCL , and EoRDR genes were upregulated in response to multiple abiotic stresses, while some exhibited unique responses, suggesting potential specialized regulatory functions. Conclusion In this study, we performed a comprehensive genome‑wide identification, and phylogenetic and expression pattern analyses of the EoAGO , EoDCL and EoRDR gene families. Our analysis provides a foundation for future research on the RNA silence elements of turfgrass, and affords scientific basis and insights for clarifying the expression patterns of EoAGO , EoDCL and EoRDR genes under adversity stress. Further functional validation and molecular breeding of these genes can be carried out for enhancing the stress resistance of centipedegrass.

Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV–GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565; The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields.

Hepatitis C virus (HCV) is a member of Hepacivirus and belongs to the family of Flaviviridae. HCV infects millions of people worldwide and may lead to cirrhosis and hepatocellular carcinoma. HCV envelope proteins, E1 and E2, play critical roles in viral cell entry and act as major epitopes for neutralizing antibodies. However, unlike other known flaviviruses, it has been challenging to study HCV envelope proteins E1E2 in the past decades as the in vitro expressed E1E2 heterodimers are usually of poor quality, making the structural and functional characterization difficult. Here we express the ectodomains of HCV E1E2 heterodimer with either an Fc-tag or a de novo designed heterodimeric tag and are able to isolate soluble E1E2 heterodimer suitable for functional and structural studies. Then we characterize the E1E2 heterodimer by electron microscopy and model the structure by the coevolution based modeling strategy with Rosetta, revealing the potential interactions between E1 and E2. Moreover, the E1E2 heterodimer is applied to examine the interactions with the known HCV receptors, neutralizing antibodies as well as the inhibition of HCV infection, confirming the functionality of the E1E2 heterodimer and the binding profiles of E1E2 with the cellular receptors. Therefore, the expressed E1E2 heterodimer would be a valuable target for both viral studies and vaccination against HCV.

Five novel ionic liquids (ILs), 1,3-dibutylimidazolium bromide [BBMIm][Br], 1-pentyl-3-butylimidazolium bromide [BPMIm][Br], 1-hexyl-3-butylimidazolium bromide [BHMIm][Br], 1,1'-(butane-1,4-diyl)bis(3-butylimidazolium) bromide [C4(BMIm)2][Br2], and 1,1'-(butane-1,4-diyl)bis(3-methylimidazolium) bromide [C4(MIm)2][Br2], were prepared and used in situ to react with bis(trifluoromethane)sulfonamide lithium salt to extract the myclobutanil, tebuconazole, cyproconazole, and prothioconazole from water samples. The results showed that mono-cationic ILs had much better recovery than dicationic ILs, and mono-imidazolium IL bearing butyl groups at N-1 and N-3 sites had the best recovery. When the length of the alkyl substituent group was more than four carbons at N-3 site, the recovery decreased with increase of alkyl chain length of 1-butylimidazolium IL. The extraction efficiency order of triazoles from high to low was [BBMIm][Br], [BPMIm][Br], [BHMIm][Br], [BMIm][Br] (1-butyl-3-methylimidazolium bromide), [C4(BMIm)2]Br2, [C4(MIm)2]Br2. An in situ ionic liquid dispersive liquid–liquid microextraction combined with ultrasmall superparamagnetic Fe3O4 was established as a pretreatment method for enrichment of triazole fungicides in water samples by using the synthetic [BBMIm][Br] as the cationic IL and used to detect analytes followed by high-performance liquid chromatography. Under the optimized conditions, the proposed method showed a good linearity within a range of 5–250 μg L−1, with the determination coefficient (r2) varying from 0.998 to 0.999. High mean enrichment factors were achieved ranging from 187 to 323, and the recoveries of the target analytes from real water samples at spiking levels of 10.0, 20.0, and 50.0 μg L−1 were between 70.1% and 115.0%. The limits of detection for the analytes were 0.74–1.44 μg L−1, and the intra-day relative standard deviations varied from 5.23% to 8.65%. The proposed method can be further applied to analyze and monitor pesticides in other related samples.Graphical AbstractThe scheme of the in-situ DLLME method for the determination of triazoles using the imidazolium-based ionic liquids

Locust is esteemed as a traditional Chinese medicine, as well as one of the most important nutritional foods especially in Asian countries. However, some toxic secondary metabolites such as mycotoxins are usually found in different parts of locust to affect its quality and safety. This study aimed to investigate the aflatoxins (AFs) contaminated parts by observing , spores' diameter, amount and distribution on head, tentacle, wing, belly and shank parts of the locust with scanning electron microscopy (SEM). Furthermore, to assess the residue levels of multi-mycotoxins in the locust, the high performance liquid chromatography with fluorescence detection (HPLC-FLD) was adopted. The technique was used to determine the contents of AFs, zearalenone (ZON) and α-zearalenol (α-ZOL) in locust and the positive samples were confirmed by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The chromatographic conditions, MS/MS parameters and the method of sample extraction were carefully optimized. Results revealed that obvious differences of strains and spores were found, while the spores' diameter ranged from 3.0 to 13.0 μm in different contaminated parts of the locust samples. The HPLC-FLD method for multi-mycotoxins analysis showed good selectivity, linearity, recovery and precision. Limits of quantification (LOQs) were lower than 27.6 μg/kg, while limits of detection (LODs) were in the range of 0.02-8.6 μg/kg. The accuracy of the developed method was validated regarding recoveries of 80.1-118.1% with relative standard deviation (RSD) ≤ 11.4%. Finally, the developed multi-mycotoxin method was applied for screening of these mycotoxins in 11 commercial locust samples. Only AFB and AFB were found in six samples, and the contamination levels ranged from 0.12 to 4.4 μg/kg, which were lower than the maximum residue limit and can be used safely. This is the first report on the exploration of contamination parts and levels of multi-mycotoxins in medicinal and edible locust. The combined method of SEM and HPLC-FLD exhibited advantages of low cost, high sensitivity, rapid determination, convenience and especially intuitive judgment, which is proposed for contamination parts observation, for the large-scale quantification of multi-mycotoxins in other medicinal animal matrices.

SQUAMOSA promoter-binding protein-like (SPL) transcription factors play a critical role in the regulation of gene expression and are indispensable in orchestrating plant growth and development while also improving resistance to environmental stressors. Although it has been identified across a wide array of plant species, there have been no comprehensive studies on the SPL gene family in centipedegrass [Eremochloa ophiuroides (Munro) Hack.], which is an important warm-season perennial C4 turfgrass. In this study, 19 potential EoSPL genes in centipedegrass were identified and assigned the names EoSPL1-EoSPL19. Gene structure and motif analysis demonstrated that there was relative consistency among the branches of the phylogenetic tree. Five pairs of segmental duplication events were detected within centipedegrass. Ten EoSPL genes were predicted to be targeted by miR156. Additionally, the EoSPL genes were found to be predominantly expressed in leaves and demonstrated diverse responses to abiotic stress (salt, drought, glufosinate ammonium, aluminum, and cold). This study offers a comprehensive insight into the SPL gene family in centipedegrass, creating a foundation for elucidating the functions of EoSPL genes and investigating their involvement in abiotic stress responses.

The cyclic AMP-dependent protein kinase A signaling pathway plays a major role in regulating plant infection by the rice blast fungus Magnaporthe oryzae. Here, we report the identification of two novel genes, MoSOM1 and MoCDTF1, which were discovered in an insertional mutagenesis screen for non-pathogenic mutants of M. oryzae. MoSOM1 or MoCDTF1 are both necessary for development of spores and appressoria by M. oryzae and play roles in cell wall differentiation, regulating melanin pigmentation and cell surface hydrophobicity during spore formation. MoSom1 strongly interacts with MoStu1 (Mstu1), an APSES transcription factor protein, and with MoCdtf1, while also interacting more weakly with the catalytic subunit of protein kinase A (CpkA) in yeast two hybrid assays. Furthermore, the expression levels of MoSOM1 and MoCDTF1 were significantly reduced in both Δmac1 and ΔcpkA mutants, consistent with regulation by the cAMP/PKA signaling pathway. MoSom1-GFP and MoCdtf1-GFP fusion proteins localized to the nucleus of fungal cells. Site-directed mutagenesis confirmed that nuclear localization signal sequences in MoSom1 and MoCdtf1 are essential for their sub-cellular localization and biological functions. Transcriptional profiling revealed major changes in gene expression associated with loss of MoSOM1 during infection-related development. We conclude that MoSom1 and MoCdtf1 functions downstream of the cAMP/PKA signaling pathway and are novel transcriptional regulators associated with cellular differentiation during plant infection by the rice blast fungus.