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Huanglongbing (citrus greening) is one of the most serious diseases of citrus. Movement of the disease occurs as a result of natural transmission by the insect vector and by movement of infected plant material. We demonstrate here that Diaphorina citri Kuwayama, the vector of citrus greening pathogens, can be transported in trailers of unprocessed fruit. Several samples of D. citri collected from trailers of oranges were positive for citrus greening pathogens, indicating that the disease can spread widely with the movement of the mature fruit as a result of moving the vectors. While movement of disease and the vector through plant materials into new areas has been recognized, our findings emphasize the need to evaluate the importance of long distance movement of psyllids on unprocessed fruit, even in the absence of vegetative tissue.

In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and precise data necessary for these tasks. With the agricultural sector increasingly relying on technological advancements, the integration of innovative solutions is essential. This study presents a novel approach that combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs). The proposed approach demonstrates superior real-time capabilities in fruit detection and counting, utilizing a combination of AI techniques and multi-UAV systems. The core innovation of this approach is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and, ultimately, a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring the high-resolution and accurate detection of targeted objects during UAV operations. Its effectiveness is proven by experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate at 14.7% and a false negative rate at 18.3%, even under challenging weather conditions like cloudiness. Overall, the practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0.

Passion fruit has a difficult natural fruiting process and low yield due to its approach herkogamy. Understanding the molecular mechanism of passion fruit style bending is crucial as it enhances pollination efficiency by optimizing stigma-anther alignment, overcoming herkogamy and improving fruit yield. In this study, we used the purple passion fruit variety ‘Tainong 1’ as experimental material. We observed that its style bends into an “S”-shape after flowering. As development progresses, the degree of this S-shaped curvature gradually weakens. Simultaneously, the style also exhibits a spatial bending from top to bottom during this process. In order to identify the genes specifically induced during the style bending process, transcriptome analysis was conducted on styles at T 1 (flowering), T 2 (30 min post-flowering), and T 5 (120 min post-flowering). 226 DEGs were identified to be specifically expressed in the T 1 to T 2 stages, mainly enriched in the “Protein processing in endoplasmic reticulum” pathway, involving heat shock proteins and DnaJ family proteins, suggesting that heat stress response is involved in early style movement regulation. The enriched 18 DEGs were all heat shock protein family proteins and DnaJ family proteins, which may be due to the stress response of the style to environmental heat stimulation. At the same time, 1,080 DEGs were specifically expressed in the T 2 to T 5 stages, mainly enriched in the “Phenylpropanoid biosynthesis” pathway and involved in the lignin biosynthesis pathway. Physiological testing confirmed that as the S-shaped of the style weakens, lignin accumulates significantly, increasing by 60% from T 1 to T 2 and by 43% from T 2 to T 5 , which is consistent with the trend of transcriptome data. In addition, differential expression of hormone metabolism related genes (abscisic acid, brassino steroids, jasmonic acid, and gibberellin pathways) was observed. Endogenous hormone quantification further confirmed hormonal regulation of style bending, revealing distinct accumulation patterns: ABA showed a biphasic trend with an initial 30% increase (T 1 to T 2 ) followed by a 9% rise (T 2 to T 5 ), while jasmonic acid exhibited a sharper 30% (T 1 to T 2 ) to 22% (T 2 to T 5 ) escalation. In summary, our results indicate that lignin accumulation enhances the mechanical strength of the style and reduces its inherent S-shaped bending. Thermal stimulation and hormone synthesis metabolism may also play a role in the process of style bending, but further research is needed to determine the specific mechanisms by which the response is regulated.

Salinity limits crop productivity, in part by decreasing shoot concentrations of the growth-promoting and senescence-delaying hormones cytokinins. Since constitutive cytokinin overproduction may have pleiotropic effects on plant development, two approaches assessed whether specific root-localized transgenic IPT (a key enzyme for cytokinin biosynthesis) gene expression could substantially improve tomato plant growth and yield under salinity: transient root IPT induction (HSP70::IPT) and grafting wild-type (WT) shoots onto a constitutive IPT-expressing rootstock (WT/35S::IPT). Transient root IPT induction increased root, xylem sap, and leaf bioactive cytokinin concentrations 2- to 3-fold without shoot IPT gene expression. Although IPT induction reduced root biomass (by 15%) in control (non-salinized) plants, in salinized plants (100 mM NaCl for 22 d), increased cytokinin concentrations delayed stomatal closure and leaf senescence and almost doubled shoot growth (compared with WT plants), with concomitant increases in the essential nutrient K⁺ (20%) and decreases in the toxic ion Na⁺ (by 30%) and abscisic acid (by 20-40%) concentrations in transpiring mature leaves. Similarly, WT/35S::IPT plants (scion/rootstock) grown with 75 mM NaCl for 90 d had higher fruit trans-zeatin concentrations (1.5- to 2-fold) and yielded 30% more than WT/non-transformed plants. Enhancing root cytokinin synthesis modified both shoot hormonal and ionic status, thus ameliorating salinity-induced decreases in growth and yield.

Ellagic acid (EA) is a naturally occurring polyphenolic compound endowed with strong antioxidant and anticancer properties that is present in high quantity in a variety of berries, pomegranates, and dried fruits. The antitumor activity of EA has been mostly attributed to direct antiproliferative and apoptotic effects. Moreover, EA can inhibit tumour cell migration, extra-cellular matrix invasion and angiogenesis, all processes that are crucial for tumour infiltrative behaviour and the metastatic process. In addition, EA may increase tumour sensitivity to chemotherapy and radiotherapy. The aim of this review is to summarize the in vitro and in vivo experimental evidence supporting the anticancer activity of pure EA, its metabolites, and EA-containing fruit juice or extracts in a variety of solid tumour models. The EA oral administration as supportive therapy to standard chemotherapy has been recently evaluated in small clinical studies with colorectal or prostate cancer patients. Novel formulations with improved solubility and bioavailability are expected to fully develop the therapeutic potential of EA derivatives in the near future.

Most animals have compound eyes, with tens to thousands of lenses attached rigidly to the exoskeleton. A natural assumption is that all of these species must resort to moving either their head or their body to actively change their visual input. However, classic anatomy has revealed that flies have muscles poised to move their retinas under the stable lenses of each compound eye 1 – 3 . Here we show that Drosophila use their retinal muscles to smoothly track visual motion, which helps to stabilize the retinal image, and also to perform small saccades when viewing a stationary scene. We show that when the retina moves, visual receptive fields shift accordingly, and that even the smallest retinal saccades activate visual neurons. Using a head-fixed behavioural paradigm, we find that Drosophila perform binocular, vergence movements of their retinas—which could enhance depth perception—when crossing gaps, and impairing the physiology of retinal motor neurons alters gap-crossing trajectories during free behaviour. That flies evolved an ability to actuate their retinas suggests that moving the eye independently of the head is broadly paramount for animals. The similarities of smooth and saccadic movements of the Drosophila retina and the vertebrate eye highlight a notable example of convergent evolution. Drosophila are shown to have retinal muscles that allow them to smoothly track visual motion and also to make rapid eye movements, and the associated functions and mechanisms involved are discussed.

Melatonin (Mel) is a multifunctional signaling molecule, quantified in a wide range of higher plants. It acts as a pleiotropic molecule and is involved in numerous signaling and physiological processes, particularly under harsh environmental conditions in plants. In the present study, the defensive role of Mel was explored in salt (NaCl)-stressed tomato ( Solanum lycopersicum ) plants. Mel (50 or 100 µM) was supplied to the foliage of tomato plants at 30 days after transplantation (DAT), grown without stress, and in the presence of NaCl (100 or 150 mM) stress. Salt stress negatively affected photosynthetic efficiency, cell viability, plant growth, fruit quality, and impaired root morphology, and stomatal physiology, but induced the accumulation of reactive oxygen species ((H 2 O 2 and O 2 · − ), malondialdehyde (MDA) content, and electrolyte leakage. Interestingly, foliar supplementation of Mel to stressed and non-stressed plants lowered MDA levels, reduced oxidative stress and promoted photosynthesis, improved cell viability, stomatal behavior, and plant growth, and boosted the activity of catalase (CAT), peroxidase (POX), and superoxide dismutase (SOD), and elevated proline content which bestowed endurance and reduced the salt-induced oxidative damage.

Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because keypoint data are susceptible to high-frequency jitter that clustering algorithms can mistake for transitions between actions. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules (‘syllables’) from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to identify syllables whose boundaries correspond to natural sub-second discontinuities in pose dynamics. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq also works in multiple species and generalizes beyond the syllable timescale, identifying fast sniff-aligned movements in mice and a spectrum of oscillatory behaviors in fruit flies. Keypoint-MoSeq, therefore, renders accessible the modular structure of behavior through standard video recordings. Keypoint-MoSeq is an unsupervised behavior segmentation algorithm that extracts behavioral modules from keypoint tracking data acquired with diverse algorithms, as demonstrated in mice, rats and fruit flies. The extracted modules faithfully reflect human-annotated behaviors even though they are obtained in an unsupervised fashion.

Due to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture. Accurate and efficient target detection technology is required for mobile inspection terminals, picking robots, and intelligent sorting equipment in tomato production and management in plant factories. However, due to the limitations of computer power, storage capacity, and the complexity of the plant factory (PF) environment, the precision of small-target detection for tomatoes in real-world applications is inadequate. Therefore, we propose an improved Small MobileNet YOLOv5 (SM-YOLOv5) detection algorithm and model based on YOLOv5 for target detection by tomato-picking robots in plant factories. Firstly, MobileNetV3-Large was used as the backbone network to make the model structure lightweight and improve its running performance. Secondly, a small-target detection layer was added to improve the accuracy of small-target detection for tomatoes. The constructed PF tomato dataset was used for training. Compared with the YOLOv5 baseline model, the mAP of the improved SM-YOLOv5 model was increased by 1.4%, reaching 98.8%. The model size was only 6.33 MB, which was 42.48% that of YOLOv5, and it required only 7.6 GFLOPs, which was half that required by YOLOv5. The experiment showed that the improved SM-YOLOv5 model had a precision of 97.8% and a recall rate of 96.7%. The model is lightweight and has excellent detection performance, and so it can meet the real-time detection requirements of tomato-picking robots in plant factories.

Purpose To investigate the phytochemical uptake following human consumption of Montmorency tart cherry (L. Prunus cerasus) and influence of selected phenolic acids on vascular smooth muscle cells in vitro. Methods In a randomised, double-blinded, crossover design, 12 healthy males consumed either 30 or 60 mL of Montmorency tart cherry concentrate. Following analysis of the juice composition, venous blood samples were taken before and 1, 2, 3, 5 and 8 h post-consumption of the beverage. In addition to examining some aspects of the concentrate contents, plasma concentrations of protocatechuic acid (PCA), vanillic acid (VA) and chlorogenic (CHL) acid were analysed by reversed-phase high-performance liquid chromatography (HPLC) with diode array for quantitation and mass spectrometry detection (LCMS) for qualitative purposes. Vascular smooth muscle cell migration and proliferation were also assessed in vitro. Results Both the 30 and 60 mL doses of Montmorency cherry concentrate contained high amounts of total phenolics (71.37 ± 0.11; 142.73 ± 0.22 mg/L) and total anthocyanins (62.47 ± 0.31; 31.24 ± 0.16 mg/L), as well as large quantities of CHL (0.205 ± 0.24; 0.410 ± 0.48 mg/L) and VA (0.253 ± 0.84; 0.506 ± 1.68 mg/L). HPLC/LCMS identified two dihydroxybenzoic acids (PCA and VA) in plasma following MC concentrate consumption. Both compounds were most abundant 1–2 h post-initial ingestion with traces detectable at 8 h post-ingestion. Cell migration was significantly influenced by the combination of PCA and VA, but not in isolation. There was no effect of the compounds on cell proliferation. Conclusions These data show new information that phenolic compounds thought to exert vasoactive properties are bioavailable in vivo following MC consumption and subsequently can influence cell behaviour. These data may be useful for the design and interpretation of intervention studies investigating the health effects of Montmorency cherries