Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis.

Recent technological developments have made it possible to map the spatial organization of a tissue at the single-cell resolution. However, computational methods for analyzing spatially continuous variations in tissue microenvironment are still lacking. Here we present ONTraC as a strategy that constructs niche trajectories using a graph neural network-based modeling framework. Our benchmark analysis shows that ONTraC performs more favorably than existing methods for reconstructing spatial trajectories. Applications of ONTraC to public spatial transcriptomics datasets successfully recapitulated the underlying anatomical structure, and further enabled detection of tissue microenvironment-dependent changes in gene regulatory networks and cell-cell interaction activities during embryonic development. Taken together, ONTraC provides a useful and generally applicable tool for the systematic characterization of the structural and functional organization of tissue microenvironments.

BCKDK Modification Enhances the Anticancer Efficacy of CAR-T Cells by Reprogramming Branched Chain Amino Acid Metabolism.

Altered Branched Chain Amino Acids (BCAA), including leucine, isoleucine and valine, are frequently observed in patients with advanced cancer. We evaluated the efficacy of Chimeric Antigen Receptor (CAR) T cell-mediated cancer cell lysis potential in the immune microenvironment of BCAA supplementation and deletion. BCAA supplementation increased cancer cell killing percentage, while accelerating BCAA catabolism and deceasing BCAA transporter decreased cancer cell lysis efficacy. We thus designed BCKDK engineering CAR T cells for the reprogramming of BCAA metabolism in the tumor microenvironment based on the genotype and phenotype modification. BCKDK overexpression (OE) in CAR-T cells significantly improved cancer cell lysis, while BCKDK knockout (KO) resulted in inferior lysis potential. In an in vivo experiment, BCKDK-OE CAR-T cells treatment significantly prolonged the survival of mice bearing NALM6-GL cancer cells, with the differentiation of central memory cells and the increasing proportion of CAR-T cells in peripheral circulation. BCKDK-KO CAR-T cells treatment resulted in shorter survival and decreasing percentage of CAR-T cells in peripheral circulation. In conclusion, BCKDK engineered CAR-T cells exert distinct phenotype for the superior anticancer efficiency.

Dl-3-n-butylphthalide promotes microglial phagocytosis and inhibits microglial inflammation via regulating AGE-RAGE pathway in APP/PS1 mice.

Alzheimer's disease (AD) stands as the most prevalent neurodegenerative condition worldwide, and its correlation with microglial function is notably significant. Dl-3-n-butylphthalide (NBP), derived from the seeds of Apium graveolens L. (Chinese celery), has demonstrated the capacity to diminish Aß levels in the brain tissue of Alzheimer's transgenic mice. Despite this, its connection to neuroinflammation and microglial phagocytosis, along with the specific molecular mechanism involved, remains undefined. In this study, NBP treatment exhibited a substantial improvement in learning deficits observed in AD transgenic mice (APP/PS1 transgenic mice). Furthermore, NBP treatment significantly mitigated the total cerebral Aß plaque deposition. This effect was attributed to the heightened presence of activated microglia surrounding Aß plaques and an increase in microglial phagocytosis of Aß plaques. Transcriptome sequencing analysis unveiled the potential involvement of the AGE (advanced glycation end products) -RAGE (receptor for AGE) signaling pathway in NBP's impact on APP/PS1 mice. Subsequent investigation disclosed a reduction in the secretion of AGEs, RAGE, and proinflammatory factors within the hippocampus and cortex of NBP-treated APP/PS1 mice. In summary, NBP alleviates cognitive impairment by augmenting the number of activated microglia around Aß plaques and ameliorating AGE-RAGE-mediated neuroinflammation. These findings underscore the related mechanism of the crucial neuroprotective roles of microglial phagocytosis and anti-inflammation in NBP treatment for AD, offering a potential therapeutic target for the disease.

Glycogen synthesis is required for adaptive thermogenesis in beige adipose tissue and affects diet-induced obesity.

Glycogen is a form of energy storage for glucose in different tissues such as liver and skeletal muscle. It remains incompletely understood how glycogen impacts on adipose tissue functionality. Cold exposure elevated the expression of Gys1 that encodes glycogen synthase 1 in brown adipose tissue (BAT) and inguinal white adipose tissue (iWAT). The in vivo function of Gys1 was analyzed using a mouse model in which Gys1 was deleted specifically in adipose tissues. Under normal chow conditions, Gys1 deletion caused little changes to body weight and glucose metabolism. Deletion of Gys1 abrogated upregulation of UCP1 and other thermogenesis-related genes in iWAT upon prolonged cold exposure or treatment with ß3-adrenergic receptor agonist CL-316,243. Stimulation of UCP1 by CL-316,243 in adipose-derived stromal cells (stromal vascular fractions, SVFs) was also reduced by Gys1 deletion. Both the basal glycogen content and CL-316,243-stimulated glycogen accumulation in adipose tissues were reduced by Gys1 deletion. High-fat diet-induced obesity and insulin resistance were aggravated in Gys1-deleted mice. The loss of body weight upon CL-316,243 treatment was also abrogated by the loss of Gys1. In conclusion, our results underscore the pivotal role of glycogen synthesis in adaptive thermogenesis in beige adipose tissue and its impact on diet-induced obesity in mice.NEW & NOTEWORTHY Glycogen is one of major types of fuel reserve in the body and its classical function is to maintain blood glucose level. This study uncovers that glycogen synthesis is required for beige fat tissue to generate heat upon cold exposure. Such a function of glycogen is linked to development of high-fat diet-induced obesity, thus extending our understanding about the physiological functions of glycogen.

Orthogonal multimodality integration and clustering in single-cell data.

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.

Enhanced YOLO v3 for precise detection of apparent damage on bridges amidst complex backgrounds.

A bridge disease identification approach based on an enhanced YOLO v3 algorithm is suggested to increase the accuracy of apparent disease detection of concrete bridges under complex backgrounds. First, the YOLO v3 network structure is enhanced to better accommodate the dense distribution and large variation of disease scale characteristics, and the detection layer incorporates the squeeze and excitation (SE) networks attention mechanism module and spatial pyramid pooling module to strengthen the semantic feature extraction ability. Secondly, CIoU with better localization ability is selected as the loss function for training. Finally, the K-means algorithm is used for anchor frame clustering on the bridge surface disease defects dataset. 1363 datasets containing exposed reinforcement, spalling, and water erosion damage of bridges are produced, and network training is done after manual labelling and data improvement in order to test the efficacy of the algorithm described in this paper. According to the trial results, the YOLO v3 model has enhanced more than the original model in terms of precision rate, recall rate, Average Precision (AP), and other indicators. Its overall mean Average Precision (mAP) value has also grown by 5.5%. With the RTX2080Ti graphics card, the detection frame rate increases to 84 Frames Per Second, enabling more precise and real-time bridge illness detection.

Analysis of Ionomic Profiles of Spinal Cords in a Rat Model with Bone Cancer Pain.

Background: Ionomics is used to study levels of ionome in different states of organisms and their correlations. Bone cancer pain (BCP) severely reduces quality of life of patients or their lifespan. However, the relationship between BCP and ionome remains unclear. Methods: The BCP rat model was constructed through inoculation of Walker 256 cells into the left tibia. Von Frey test, whole-cell patch-clamp recording and inductively coupled plasma mass spectrometry (ICP-MS) technologies were conducted for measuring tactile hypersensitivity, the frequency and amplitude of miniature excitatory postsynaptic currents (mEPSCs) of neurons of spinal slices, and ionome of spinal cord samples, respectively. Principal component analysis (PCA) was used to explore ionomic patterns of the spinal cord. Results: The BCP rat model was successfully constructed through implantation of Walker 256 cells into the left tibia. The frequency and amplitude of mEPSCs of neurons in the spinal cord slices from the BCP model rats were notably greater than those in the sham control. In terms of ionomics, the spinal cord levels of two macroelements (Ca and S), four microelements (Fe, Mn, Li and Sr) and the toxic element Ti in the BCP group of rats were significantly increased by inoculation of Walker 256 cancer cells, compared to the sham control. In addition, the correlation patterns between the elements were greatly changed between the sham control and BCP groups. PCA showed that inoculation of Walker 256 cells into the tibia altered the overall ionomic profiles of the spinal cord. There was a significant separation trend between the two groups. Conclusion: Taken together, inoculation of Walker 256 cells into the left tibia contributes to BCP, which could be closely correlated by some elements. The findings provided novel information on the relationship between the ionome and BCP.

Comparative Study of Ultrasonic Vibration-Assisted Die-Sinking Micro-Electrical Discharge Machining on Polycrystalline Diamond and Titanium.

Die-sinking micro-electrical discharge machining (micro-EDM) is a potential method used to fabricate intricate structures without complex electrode motion planning and compensation. However, machining efficiency and poor discharge states are still bottlenecks. This study conducted a comparative investigation into the impact of ultrasonic vibration on die-sinking micro-EDM of polycrystalline diamond (PCD) and pure titanium (TA2). By adjusting discharge parameters, this study systematically evaluated the influence of ultrasonic vibration on these two materials based on discharge waveforms, motion trajectories, effective discharge counts and groove profiles. At an open-circuit voltage of 100 V, ultrasonic vibration promotes die-sinking micro-EDM of PCD. However, when the open-circuit voltage increases to 200 V, ultrasonic vibration exhibits inhibitory effects in general. Conversely, for TA2, ultrasonic vibration shows a promoting effect at both voltages, indicating the differences of ultrasonic vibration-assisted die-sinking micro-EDM on PCD and TA2. For PCD, ultrasonic cavitation improves the discharge gap environment, accelerating the removal of discharge debris. For TA2, due to its poor thermal conductivity, ultrasonic cavitation acts to break the arc, accelerating heat transfer. These research findings provide guidance for ultrasonic vibration-assisted die-sinking micro-EDM in industrial applications.

Effect of zein-pectin composite particles on the stability and rheological properties of gelatin/hydroxypropyl methylcellulose water-water systems.

To improve the compatibility of gelatin (GA) and hydroxypropyl methylcellulose (HPMC), we investigated the effects of zein-pectin composite particles (ZCPs) with various zein/pectin ratios (1:0, 1:0.5, 1:1, 1:1.5, and 1:2) on the physical stability, microstructure, and rheological properties of the GA/HPMC water-water systems. With increasing pectin ratio, the particle size of the composite particles increased from 234.53 ± 1.48 nm to 1111.00 ± 26.91 nm, and their zeta potential decreased from 20.60 mV to below -34.77 mV. Macroscopic and microstructure observations indicated that pectin-modified ZCPs could effectively inhibit phase separation behavior between GA and HPMC. Compared to pure HPMC, the GA/HPMC water-water systems possessed a higher viscosity and dynamic modulus at room temperatures but lower gel temperatures (reduction of about 11 %). The viscosity and modulus of the water-water systems increased with increasing pectin ratio in ZCPs. However, the ratio had no impact on the gel-sol (sol-gel) transition temperatures (not statistically significant (P < 0.05)). This study may serve as a reference for advancing the processability of HPMC.

Effect of initial moisture content, resulting from different ratios of vegetable waste to maize straw, on compost was mediated by composting temperatures and microbial communities at low temperatures.

Owing to the huge amounts and perishable character of vegetable wastes, composting is one of the best options for recycling vegetable wastes post-harvest. The initial moisture content (MC) is critical for optimizing composting process, but the effect of high MC in undehydrated vegetable wastes on composting was rarely reported. For this, the plant-scale windrows were prepared by mixing cauliflower waste and maize straw at different ratios to control initial MC of 70 % (T1-70) and 80 % (T2-80), respectively, and composted in winter. As composting progressed, substantial organic matter degradation, progressive humification, decreases in electrical conductivity and increases of pH and germination index (GI) were observed in both treatments. Nonetheless, T1-70 accelerated heating rate early during composting, prolonged high temperature period (>50 °C) by 30 d, thus increased the harmless level of composting, and significantly improved the humification of end-products compared to T2-80. Results also revealed that T1-70 activated more indigenous microbes and enhanced microbial interactions early during composting, with the fungi enriched in T1-70 playing an important role in accelerating the composting process. Remarkably, the difference in composting temperatures, humification degree, and microbial communities between the two treatments was most significant during the maturation phase. In this phase, MWH_CFBk5, Planktosalinus, Pseudopedobacter, and Luteimonas enriched in T1-70 were positively correlated with humification indices. It is suggested that the effect of initial MC, resulting from different ratios of vegetable waste to maize straw, on their composting was mediated by the composting temperature and microbial communities at low temperatures.

Ultra short term power load forecasting based on the fusion of Seq2Seq BiLSTM and multi head attention mechanism.

Ultra-short-term power load forecasting is beneficial to improve the economic efficiency of power systems and ensure the safe and stable operation of power grids. As the volatility and randomness of loads in power systems, make it difficult to achieve accurate and reliable power load forecasting, a sequence-to-sequence based learning framework is proposed to learn feature information in different dimensions synchronously. Convolutional Neural Networks(CNN) Combined with Bidirectional Long Short Term Memory(BiLSTM) Networks is constructed in the encoder to extract the correlated timing features embedded in external factors affecting power loads. The parallel BiLSTM network is constructed in the decoder to mine the power load timing information in different regions separately. The multi-headed attention mechanism is introduced to fuse the BiLSTM hidden layer state information in different components to further highlight the key information representation. The load forecastion results in different regions are output through the fully connected layer. The model proposed in this paper has the advantage of high forecastion accuracy through the example analysis of real power load data.

First report of Boeremia exigua causing spot blight on cowpea in China.

Cowpea or black-eyed pea [Vigna unguiculata (L.) Walp.] is a dual-purpose leguminous crop grown for food and fodder. In September 2022, cowpea plants exhibiting symptoms of a leaf spot and blight were observed in Renda Town located in Jingning County of Gansu Province, China, with the disease incidence in individual cowpea fields as high as 100%. Diseased leaves showed variable-sized, nearly circular brown blotches, large blotches with dark brown margins, and the adaxial surfaces of blotches had small black dots and whorls (Fig. 1). Multiple isolates with consistent colony characteristics were obtained from cowpea leaves with typical symptoms. The isolates were transferred to fresh potato dextrose agar medium (PDA) and then purified by transferring hyphal tips to PDA. Three isolates, JNJD-1, JNJD-2, and JNJD-3, were selected for subsequent identification and pathogenicity determination. After eight days at 25℃ on PDA, the colonies appeared irregular, aerial mycelium dense, cottony, atrovirens to olive brown, with white hyphae on the undulate margin (Fig. 2A and B). The pycnidia were globose to sub-globose, brown to dark brown, with 70-110 µm diameters. Single celled hyaline conidia were ellipsoidal to oblong with obtuse ends, and measured 6.6-9.3 × 2.8-4.1 µm (x̄ = 7.8 × 3.5 µm, n = 50) (Fig. 2C). Morphological characteristics are similar to the description of the genus Boeremia (Aveskamp et al, 2010). Primer pairs ITS1/ITS4, LR0R/LR5, fRPB2-5F2/fRPB2-7cR, and TUB2FD/TUB4RD were used to amplify portions of the ITS, LSU, RPB2, and TUB genes, respectively (Chen et al, 2015). The obtained sequences (Accession numbers: PP033662 to PP033664 for ITS, PP033667 to PP033669 for LSU, PP035531 to PP035533 for RPB2, and PP035534 to PP035536 for TUB) were 97% identical to that of a B. exigua strain CBS 431.74 (accession no. FJ427001, EU754183, GU371780, and FJ427112) (Table 1). The constructed maximum likelihood tree indicated close relationships between three isolates and B. exigua, which clustered together (Fig. 3). Cowpea plants (cultivar Junlintianxia) at the three-leaf stage were inoculated by spraying a spore suspension (1×106 conidia/ml) of JNJD-1, JNJD-2, and JNJD-3 until run off and incubated at greenhouse conditions (25°C and 12 h light). Inoculations with sterile water were used as a control and each treatment was repeated 3 times with five plants per replicate. Small brown spots appeared on the infected leaves at 2 dpi, followed by the appearance of large blotches, with dark brown at the margin and grayish-white in the center at 5 dpi (Fig. 4A). These lesions gradually increase and coalesce, causing leaf chlorosis and finally defoliation in serious cases. Disease incidence in inoculated cowpea plants treated with the isolates JNJD-1, JNJD-2, and JNJD-3 reached almost 100%. In contrast, control plants developed no symptoms (Fig. 4B). The pathogens were re-isolated from the inoculated leaves and identified as B. exigua using morphological and molecular analysis, whereas no fungus was isolated from control leaves. The experiment was repeated once under the same conditions, yielding similar results. B. exigua has a broad host range, infecting 19 families and 31 genera of plant species, and causing leaf spots, leaf blight, and tuber rot (Lan and Duan 2022). To our knowledge, this is the first report of the pathogen B. exigua causing spot blight on cowpeas. It has been reported that B. exigua infects leguminous crops from multiple genera, such as field pea, soybean, white clover, and Dumasia villosa (Liu et al, 2023). This study further enriches the host range of this pathogen and the pathogen species of cowpea leaf diseases.

Design and Analysis of the Dual-Band Far-Field Super-Resolution Metalens with Large Aperture.

The resolving power of metalens telescopes rely on their aperture size. Flat telescopes are advancing with the research on super-resolution confocal metalenses with large aperture. However, the aperture sizes of metalenses are usually bound within hundreds of micrometers due to computational and fabrication challenges, limiting their usage on practical optical devices like telescopes. In this work, we demonstrated a two-step designing method for the design of dual-band far-field super-resolution metalens with aperture sizes from the micro-scale to macro-scale. By utilizing two types of inserted unit cells, the phase profile of a dual-wavelength metalens with a small aperture of 100 µm was constructed. Through numerical simulation, the measured FWHM values of the focal spots of 5.81 µm and 6.81 µm at working wavelengths of 632.8 nm and 1265.6 nm were found to all be slightly smaller than the values of 0.61 λ/NA, demonstrating the super-resolution imaging of the designed metalens. By measuring the optical power ratio of the focal plane and the incident plane, the focusing efficiencies were 76% at 632.8 nm and 64% at 1265.6 nm. Based on the design method for small-aperture metalens, far-field imaging properties through the macro metalens with an aperture of 40 mm were simulated by using the Huygens-Fresnel principle. The simulation results demonstrate confocal far-field imaging behavior at the target wavelengths of 632.8 nm and 1265.6 nm, with a focal length of 200 mm. The design method for dual-band far-field super-resolution metalens with a large aperture opens a door towards the practical applications in the dual-band space telescope system.

Validation of the IMPEDE VTE score for prediction of venous thromboembolism in Chinese patients with multiple myeloma: A single-center retrospective cohort study.

Multiple myeloma (MM) significantly increases the risk of venous thromboembolism (VTE) within 6 months of treatment initiation. The IMPEDE VTE score is a VTE risk prediction model which is recently incorporated into the National Comprehensive Cancer Network (NCCN) guidelines, but it lacks validation among Asians, including Chinese MM patients. We performed a retrospective chart review of 405 Chinese with newly diagnosed MM who started therapy at Beijing Jishuitan Hospital between April 2013 to October 2022. The 6-month cumulative incidence of VTE was 3.8 % (95 % CI:1.6-7.6), 8.6 % (95 % CI: 5.3-21.9) and 40.5 % (95 % CI: 24.9-55.7) in the low-, intermediate- and high-risk groups (P < 0.001), respectively. The C-statistic of the IMPEDE VTE scores for predicting VTE within 6 months of treatment initiation was 0.74 (95 % CI: 0.65-0.83). Of note, in this single-center cohort study, we propose that the anticoagulant LMWH may be more effective than the antiplatelet aspirin in potentially preventing VTE in newly diagnosed MM patients. Our findings suggest that the IMPEDE VTE score is a valid evidence-based risk stratification tool in Chinese patients with newly diagnosed MM.

Dual-constrained physics-enhanced untrained neural network for lensless imaging.

An untrained neural network (UNN) paves a new way to realize lensless imaging from single-frame intensity data. Based on the physics engine, such methods utilize the smoothness property of a convolutional kernel and provide an iterative self-supervised learning framework to release the needs of an end-to-end training scheme with a large dataset. However, the intrinsic overfitting problem of UNN is a challenging issue for stable and robust reconstruction. To address it, we model the phase retrieval problem into a dual-constrained untrained network, in which a phase-amplitude alternating optimization framework is designed to split the intensity-to-phase problem into two tasks: phase and amplitude optimization. In the process of phase optimization, we combine a deep image prior with a total variation prior to retrain the loss function for the phase update. In the process of amplitude optimization, a total variation denoising-based Wirtinger gradient descent method is constructed to form an amplitude constraint. Alternative iterations of the two tasks result in high-performance wavefield reconstruction. Experimental results demonstrate the superiority of our method.

Notoginsenoside R1 targets PPAR-γ to inhibit hepatic stellate cell activation and ameliorates liver fibrosis.

BACKGROUND: Hepatic fibrosis, a common pathological process that occurs in end-stage liver diseases, is a serious public health problem and lacks effective therapy. Notoginsenoside R1 (NR1) is a small molecule derived from the traditional Chinese medicine Sanqi, exhibiting great potential in treating diverse metabolie disorders. Here we aimed to enquired the role of NR1 in liver fibrosis and its underlying mechanism in hepatoprotective effects. METHODS: We investigated the anti-fibrosis effect of NR1 using CCl4-induced mouse mode of liver fibrosis as well as TGF-ß1-activated JS-1, LX-2 cells and primary hepatic stellate cell. Cell samples treated by NR1 were collected for transcriptomic profiling analysis. PPAR-γ mediated TGF-ß1/Smads signaling was examined using PPAR-γ selective inhibitors and agonists intervention, immunofluorescence staining and western blot analysis. Additionally, we designed and studied the binding of NR1 to PPAR-γ using molecular docking. RESULTS: NR1 obviously attenuated liver histological damage, reduced serum ALT, AST levels, and decreased liver fibrogenesis markers in mouse mode. Mechanistically, NR1 elevated PPAR-γ and decreased TGF-ß1, p-Smad2/3 expression. The TGF-ß1/Smads signaling pathway and fibrotic phenotype were altered in JS-1 cells after using PPAR-γ selective inhibitors and agonists respectively, confirming PPAR-γ played a pivotal protection role inNR1 treating liver fibrosis. Further molecular docking indicated NR1 had a strong binding tendency to PPAR-γ with minimum free energy. CONCLUSIONS: NR1 attenuates hepatic stellate cell activation and hepatic fibrosis by elevating PPAR-γ to inhibit TGF-ß1/Smads signalling. NR1 may be a potential candidate compound for reliving liver fibrosis.

The Effects of Family and School Interpersonal Relationships on Depression in Chinese Elementary School Children: The Mediating Role of Academic Stress and the Moderating Role of Self-Esteem.

This study explores the relative contributions and psychological mechanisms of the effects of family (father-child and mother-child) and school (teacher-student and student-student) interpersonal relationships on depression in elementary school children. The participants (n = 20,629) were elementary school children (48.19% male) aged nine to 13 years from Southwest China during the COVID-19 pandemic. They voluntarily completed questionnaires regarding parent-child, teacher-student, and student-student relationships, as well as academic stress and self-esteem. The results indicate that the effect of family interpersonal relationships on children's depression was stronger than that of school interpersonal relationships. The predictive effects of father-child and mother-child relationships on children's depression did not significantly differ; however, the effect of student-student relationships was significantly stronger than that of teacher-student relationships. Academic stress partially mediated the effect of interpersonal relationships on depression in elementary school children. The effects of family interpersonal relationships and academic stress on depression were moderated by self-esteem. These findings underscore the disparities and mechanisms pertaining to the impacts of diverse interpersonal associations on children's depression, thus signifying significant implications for the advancement of research and intervention strategies aimed at addressing this issue.

An efficient genetic transformation system mediated by Rhizobium rhizogenes in fruit trees based on the transgenic hairy root to shoot conversion.

Genetic transformation is a critical tool for gene editing and genetic improvement of plants. Although many model plants and crops can be genetically manipulated, genetic transformation systems for fruit trees are either lacking or perform poorly. We used Rhizobium rhizogenes to transfer the target gene into the hairy roots of Malus domestica and Actinidia chinensis. Transgenic roots were generated within 3 weeks, with a transgenic efficiency of 78.8%. Root to shoot conversion of transgenic hairy roots was achieved within 11 weeks, with a regeneration efficiency of 3.3%. Finally, the regulatory genes involved in stem cell activity were used to improve shoot regeneration efficiency. MdWOX5 exhibited the most significant effects, as it led to an improved regeneration efficiency of 20.6% and a reduced regeneration time of 9 weeks. Phenotypes of the overexpression of RUBY system mediated red roots and overexpression of MdRGF5 mediated longer root hairs were observed within 3 weeks, suggesting that the method can be used to quickly screen genes that influence root phenotype scores through root performance, such as root colour, root hair, and lateral root. Obtaining whole plants of the RUBY system and MdRGF5 overexpression lines highlights the convenience of this technology for studying gene functions in whole plants. Overall, we developed an optimized method to improve the transformation efficiency and stability of transformants in fruit trees.

[Potential Ecological Risk Characteristics and Source Apportionment of Heavy Metals in Farmland Soils around Typical Factories in Hunan Province Based on Monte-Carlo Simulation].

In order to explore the pollution characteristics， ecological risks， and pollution sources of heavy metals in farmland soils around typical factories in Hunan Province， the content characteristics of eight heavy metals in farmland soils around fluoride factories， leather factories， and plating plants were analyzed. The geo-accumulation index and potential ecological risk index were used to evaluate the pollution and environmental risk of heavy metals. The correlation analysis， hierarchical cluster analysis， and principal component analysis were used to analyze the sources of heavy metals. The Monte-Carlo model was used to evaluate the probability risk of regional ecological risk. The results showed that the main pollution elements in the soil were Cd and Zn， and their mean values were 4.46 and 2.73 times the background values， respectively. Zn was at a mild pollution level in the soil of the three typical factories， and Cd was at a moderate pollution level in the whole fluoride factory. The pollution sources of heavy metals in the typical factories were mainly natural sources， industrial activity sources （industrial waste discharge， mineral mining， and smelting activities）， traffic sources， etc. The results of potential ecological risk assessment showed that the ecological risk of the fluoride factory was at a high risk level， and the ecological risk of the leather factory and plating plants was at a high risk level. Cd was the main contributing element. The results of Monte-Carlo probabilistic ecological risk assessment reduced the uncertainty of deterministic assessment， which could provide scientific basis for accurate risk management and control in the regions.