[Ecological Environment Dynamical Evaluation of Hutuo River Basin Using Remote Sensing].

Hutuo River Basin straddles Shanxi and Hebei provinces, and Hutuo River was once cut off due to economic development and urban expansion after 2000; however, with the national emphasis on ecological civilization and the implementation of the South-North Water Diversion Project, the ecological protection of Hutuo River Basin has been significantly improved. MODIS data, Landsat data, and night light remote sensing data were selected based on the google earth engine (GEE) platform, and a new evaluation index system was generated by combining the biological richness index, vegetation cover index, land stress index, and pollution load index in the ecological environment index (EI) and the humidity index in the remote sensing ecological index (RSEI), using the variation coefficient method and entropy weighting method to assign weights to these indices. An ecological environment evaluation model was constructed to evaluate and classify the ecological environment quality of Hutuo River Basin from 2000 to 2020, and the driving factors were interpreted by using geographic probes. The results showed that:① on a time scale, the ecological environment of Hutuo River Basin was in a decline period from 2000 to 2015 and a recovery period from 2015 to 2020. From a grid scale, the ecological environment quality in the central part of the basin showed a state of improvement year by year, and in the western and eastern parts of the basin, the ecological environment quality in the decline period decreased year by year, whereas the ecological environment quality in the recovery period improved. ② Hot spot analysis showed that the spatial distribution of the ecological environment quality in Hutuo River Basin was high in the middle and low on both sides. Cold spot regions were mainly located in major cities and towns in the eastern and southern parts and scattered in the river valley area on the west side. ③ Geodetection analysis showed that the single factor detection drivers were mainly population density, vegetation net primary productivity (NPP), fractional vegetation cover (FVC), and geomorphological type. The dominant factor of cross-detection was "geomorphological type + FVC." With the deepening of ecological civilization construction and the implementation of Hutuo River Protection Regulations, in combination with different factors such as the natural environment and social characteristics in this basin, the research on ecological environment evaluation in Hutuo River Basin can provide data support for proposing localized policies to improve the ecological environment.

ZYNQ-Based Visible Light Defogging System Design Realization.

Under a foggy environment, the air contains a large number of suspended particles, which lead to the loss of image information and decline of contrast collected by the vision system. This makes subsequent processing and analysis difficult. At the same time, the current stage of the defogging system has problems such as high hardware cost and poor real-time processing. In this article, an image defogging system is designed based on the ZYNQ platform. First of all, on the basis of the traditional dark-channel defogging algorithm, an algorithm for segmenting the sky is proposed, and in this way, the image distortion caused by the sky region is avoided, and the atmospheric light value and transmittance are estimated more accurately. Then color balancing is performed after image defogging to improve the quality of the final output image. The parallel computing advantage and logic resources of the PL (Programmable Logic) part (FPGA) of ZYNQ are fully utilized through instruction constraints and logic optimization. Finally, the visible light detector is used as the input to build a real-time video processing experiment platform. The experimental results show that the system has a good defogging effect and meet the real-time requirements.

OsUGE2 Regulates Plant Growth through Affecting ROS Homeostasis and Iron Level in Rice.

BACKGROUND: The growth and development of rice (Oryza sativa L.) are affected by multiple factors, such as ROS homeostasis and utilization of iron. Here, we demonstrate that OsUGE2, a gene encoding a UDP-glucose 4-epimerase, controls growth and development by regulating reactive oxygen species (ROS) and iron (Fe) level in rice. Knockout of this gene resulted in impaired growth, such as dwarf phenotype, weakened root growth and pale yellow leaves. Biochemical analysis showed that loss of function of OsUGE2 significantly altered the proportion and content of UDP-Glucose (UDP-Glc) and UDP-Galactose (UDP-Gal). Cellular observation indicates that the impaired growth may result from decreased cell length. More importantly, RNA-sequencing analysis showed that knockout of OsUGE2 significantly influenced the expression of genes related to oxidoreductase process and iron ion homeostasis. Consistently, the content of ROS and Fe are significantly decreased in OsUGE2 knockout mutant. Furthermore, knockout mutants of OsUGE2 are insensitive to both Fe deficiency and hydrogen peroxide (H2O2) treatment, which further confirmed that OsUGE2 control rice growth possibly through Fe and H2O2 signal. Collectively, these results reveal a new pathway that OsUGE2 could affect growth and development via influencing ROS homeostasis and Fe level in rice.

Effects of Endogenous Antioxidants in Camellia Oil on the Formation of 2-Monochloropropane-1, 3-diol Esters and 3-Monochloropropane-1,2-diol Esters during Thermal Processing.

2-Monochloropropane-1, 3-diol (2-MCPD) esters and 3-monochloropropane-1,2-diol (3-MCPD) esters, a class of substances potentially harmful to human health, are usually formed during the refining of vegetable oils under high temperature. The effects of endogenous antioxidants in vegetable oils on the formation of 2- and 3-MCPD esters is still unknown. In this study, the effects of endogenous antioxidants (α-tocopherol, stigmasterol and squalene) on the formation of 2- and 3-MCPD esters in model thermal processing of camellia oil were investigated. The possible formation mechanism of 2- and 3-MCPD esters was also studied through the monitoring of acyloxonium ions, the intermediate ions of 2- and 3-MCPD esters formation, and free radicals by employing infrared spectra and electron paramagnetic resonance (EPR), respectively. The results indicated that the addition of α-tocopherol had either promoting or inhibiting effects on the formation of 2- and 3-MCPD esters, depending on the amount added. Stigmasterol inhibited the formation of 3-MCPD ester and 2-MCPD ester at low concentrations, while promoting their formation at high concentrations. Squalene exhibited a promotional effect on the formation of 3-MCPD ester and 2-MCPD ester, with an increased promotion effect as the amount of squalene added increased. The EPR results suggested that CCl3•, Lipid alkoxyl, N3• and SO3• formed during the processing of camellia oil, which may further mediate the formation of chlorpropanol esters. This study also inferred that squalene promotes the participation of the free radical in chlorpropanol ester formation.

Transcription factor OsMYB30 increases trehalose content to inhibit α-amylase and seed germination at low temperature.

Low-temperature germination (LTG) is an important agronomic trait for direct-seeding cultivation of rice (Oryza sativa). Both OsMYB30 and OsTPP1 regulate the cold stress response in rice, but the function of OsMYB30 and OsTPP1 in regulating LTG and the underlying molecular mechanism remains unknown. Employing transcriptomics and functional studies revealed a sugar signaling pathway that regulates seed germination in response to low temperature (LT). Expression of OsMYB30 and OsTPP1 was induced by LT during seed germination, and overexpressing either OsMYB30 or OsTPP1 delayed seed germination and increased sensitivity to LT during seed germination. Transcriptomics and qPCR revealed that expression of OsTPP1 was upregulated in OsMYB30-overexpressing lines but downregulated in OsMYB30-knockout lines. In vitro and in vivo experiments revealed that OsMYB30 bound to the promoter of OsTPP1 and regulated the abundance of OsTPP1 transcripts. Overaccumulation of trehalose (Tre) was found in both OsMYB30- and OsTPP1-overexpressing lines, resulting in inhibition of α-amylase 1a (OsAMY1a) gene during seed germination. Both LT and exogenous Tre treatments suppressed the expression of OsAMY1a, and the osamy1a mutant was not sensitive to exogenous Tre during seed germination. Overall, we concluded that OsMYB30 expression was induced by LT to activate the expression of OsTPP1 and increase Tre content, which thus inhibited α-amylase activity and seed germination. This study identified a phytohormone-independent pathway that integrates environmental cues with internal factors to control seed germination.

OsNAC2 regulates seed dormancy and germination in rice by inhibiting ABA catabolism.

Seed dormancy and germination determine the beginning of the life cycle of plants, and the phytohormone ABA plays a crucial role in regulation of seed dormancy and germination. However, the upstream regulatory mechanism of ABA metabolism during dormancy releasing is still remain elusive. In this paper, we present a novel mechanism of OsNAC2 in controlling ABA metabolism and regulation of seed dormancy. OsNAC2 highly expressed during seed development and germination, and overexpression of OsNAC2 strengthened seed dormancy and suppressed germination. Moreover, exogenous phytohormone treatment showed that OsNAC2 acted upstream of GA signaling and downstream of ABA signaling. Additionally, overexpression of OsNAC2 inhibited ABA degradation and increased ABA content during early germination. Further molecular analysis revealed that OsNAC2 directly bound to the ABA metabolism genes promoter and inhibits their transcription in rice protoplasts. These finding could help us explain the genetic regulation mechanism of ABA metabolism during dormancy release and germination in rice.

IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition.

Recently, deep learning models have been widely applied to modulation recognition, and they have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization. To complement the advantages of different modalities, we focus on the multi-modal fusion method. Therefore, we introduce an iterative dual-scale attentional fusion (iDAF) method to integrate multimodal data. Firstly, two feature maps with different receptive field sizes are constructed using local and global embedding layers. Secondly, the feature inputs are iterated into the iterative dual-channel attention module (iDCAM), where the two branches capture the details of high-level features and the global weights of each modal channel, respectively. The iDAF not only extracts the recognition characteristics of each of the specific domains, but also complements the strengths of different modalities to obtain a fruitful view. Our iDAF achieves a recognition accuracy of 93.5% at 10 dB and 0.6232 at full signal-to-noise ratio (SNR). The comparative experiments and ablation studies effectively demonstrate the effectiveness and superiority of the iDAF.

A Large Genetic Causal Analysis of the Gut Microbiota and Urological Cancers: A Bidirectional Mendelian Randomization Study.

BACKGROUND: Several observational studies and clinical trials have shown that the gut microbiota is associated with urological cancers. However, the causal relationship between gut microbiota and urological cancers remains to be elucidated due to many confounding factors. METHODS: In this study, we used two thresholds to identify gut microbiota GWAS from the MiBioGen consortium and obtained data for five urological cancers from the UK biobank and Finngen consortium, respectively. We then performed a two-sample Mendelian randomization (MR) analysis with Wald ratio or inverse variance weighted as the main method. We also performed comprehensive sensitivity analyses to verify the robustness of the results. In addition, we performed a reverse MR analysis to examine the direction of causality. RESULTS: Our study found that family Rikenellaceae, genus Allisonella, genus Lachnospiraceae UCG001, genus Oscillibacter, genus Eubacterium coprostanoligenes group, genus Eubacterium ruminantium group, genus Ruminococcaceae UCG013, and genus Senegalimassilia were related to bladder cancer; genus Ruminococcus torques group, genus Oscillibacter, genus Barnesiella, genus Butyricicoccus, and genus Ruminococcaceae UCG005 were related to prostate cancer; class Alphaproteobacteria, class Bacilli, family Family XI, genus Coprococcus2, genus Intestinimonas, genus Lachnoclostridium, genus Lactococcus, genus Ruminococcus torques group, and genus Eubacterium brachy group were related to renal cell cancer; family Clostridiaceae 1, family Christensenellaceae, genus Eubacterium coprostanoligenes group, genus Clostridium sensu stricto 1, and genus Eubacterium eligens group were related to renal pelvis cancer; family Peptostreptococcaceae, genus Romboutsia, and genus Subdoligranulum were related to testicular cancer. Comprehensive sensitivity analyses proved that our results were reliable. CONCLUSIONS: Our study confirms the role of specific gut microbial taxa on urological cancers, explores the mechanism of gut microbiota on urological cancers from a macroscopic level, provides potential targets for the screening and treatment of urological cancers, and is dedicated to providing new ideas for clinical research.

Artificial Intelligence-Assisted Left Ventricular Diastolic Function Assessment and Grading: Multiview Versus Single View.

BACKGROUND: Clinical assessment and grading of left ventricular diastolic function (LVDF) requires quantification of multiple echocardiographic parameters interpreted according to established guidelines, which depends on experienced clinicians and is time consuming. The aim of this study was to develop an artificial intelligence (AI)-assisted system to facilitate the clinical assessment of LVDF. METHODS: In total, 1,304 studies (33,404 images) were used to develop a view classification model to select six specific views required for LVDF assessment. A total of 2,238 studies (16,794 two-dimensional [2D] images and 2,198 Doppler images) to develop 2D and Doppler segmentation models, respectively, to quantify key metrics of diastolic function. We used 2,150 studies with definite LVDF labels determined by two experts to train single-view classification models by AI interpretation of strain metrics or video. The accuracy and efficiency of these models were tested in an external data set of 388 prospective studies. RESULTS: The view classification model identified views required for LVDF assessment with good sensitivity (>0.9), and view segmentation models successfully outlined key regions of these views with intersection over union > 0.8 in the internal validation data set. In the external test data set of 388 cases, AI quantification of 2D and Doppler images showed narrow limits of agreement compared with the two experts (e.g., left ventricular ejection fraction, -12.02% to 9.17%; E/e' ratio, -3.04 to 2.67). These metrics were used to detect LV diastolic dysfunction (DD) and grade DD with accuracy of 0.9 and 0.92, respectively. Concerning the single-view method, the overall accuracy of DD detection was 0.83 and 0.75 by strain-based and video-based models, and the accuracy of DD grading was 0.85 and 0.8, respectively. These models could achieve diagnosis and grading of LVDD in a few seconds, greatly saving time and labor. CONCLUSION: AI models successfully achieved LVDF assessment and grading that compared favorably with human experts reading according to guideline-based algorithms. Moreover, when Doppler variables were missing, AI models could provide assessment by interpreting 2D strain metrics or videos from a single view. These models have the potential to save labor and cost and to facilitate work flow of clinical LVDF assessment.

Assessment of an exhaled breath test using ultraviolet photoionization time-of-flight mass spectrometry for the monitoring of kidney transplant recipients.

Continuous monitoring for immunosuppressive status, infection and complications are a must for kidney transplantation (KTx) recipients. Traditional monitoring including blood sampling and kidney biopsy, which caused tremendous medical cost and trauma. Therefore, a cheaper and less invasive approach was urgently needed. We thought that a breath test has the potential to become a feasible tool for KTx monitoring. A prospective-specimen collection, retrospective-blinded assessment strategy was used in this study. Exhaled breath samples from 175 KTx recipients were collected in West China Hospital and tested by online ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOF-MS). The classification models based on breath test performed well in classifying normal and abnormal values of creatinine, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN) and tacrolimus, with AUC values of 0.889, 0.850, 0.849 and 0.889, respectively. Regression analysis also demonstrated the predictive ability of breath test for clinical creatinine, eGFR, BUN, tacrolimus level, as the predicted values obtained from the regression model correlated well with the clinical true values (p < 0.05). The findings of this investigation implied that a breath test by using UVP-TOF-MS for KTx recipient monitoring is possible and accurate, which might be useful for future clinical screenings.

Deep learning-based measurement of echocardiographic data and its application in the diagnosis of sudden cardiac death.

This study aimed to evaluate the potential of deep learning applied to the measurement of echocardiographic data in patients with sudden cardiac death (SCD). 320 SCD patients who met the inclusion and exclusion criteria underwent clinical evaluation, including age, sex, BMI, hypertension, diabetes, cardiac function classification, and echocardiography. The diagnostic value of deep learning model was observed by dividing the patients into two groups: training group (n=160) and verification group (n=160), as well as two groups of healthy volunteers (n=200 for each group) during the same period. Logistic regression analysis showed that MLVWT, LVEDD, LVEF, LVOT-PG, LAD, E/e' were all risk factors for SCD. Subsequently, a deep learning-based model was trained using the collected images of the training group. The optimal model was selected based on the identification accuracy of the validation group and showed an accuracy of 91.8%, sensitivity of 80.00%, and specificity of 91.90% in the training group. The AUC value of the ROC curve of the model was 0.877 for the training group and 0.995 for the validation groups. This approach demonstrates high diagnostic value and accuracy in predicting SCD, which is clinically important for the early detection and diagnosis of SCD.

Prognostic Factors and Survival of Chinese Patients with Cardiac Amyloidosis.

Purpose: To identify the survival and prognostic factors for cardiac amyloidosis (CA) in Chinese patients. Methods: This was a prospective cohort study of 72 patients diagnosed with CA and admitted to the PLA General Hospital between November 2017 and April 2021. Demographic, clinical, laboratory, electrocardiographic, conventional ultrasound, endocardial LS during LV systole (LV ENDO LSsys), and myocardial strain data were recorded. Survival was assessed. All-cause mortality was the endpoint. Follow-up was censored on September 30, 2021. Results: The mean follow-up was 17.1 ± 12.9 months. Among the 72 patients, 39 died, 23 survived, and 10 were lost to follow-up. Mean survival for all patients was 24.7 ± 2.2 months. Mean survival was 32.7 ± 2.4 months among patients with NYHA class II, 26.6 ± 3.4 months for NYHA class III, and 5.8 ± 1.1 months for NYHA class IV. The multivariate Cox proportional hazard regression model showed that NYHA class (HR = 3.42, 95% CI: 1.36-8.65, P = 0.002), log-proBNP level (HR = 1.40, 95% CI: 1.17-5.83, P = 0.03), and ENDO LSsys of the LV basal level (HR = 1.25, 95% CI: 1.05-1.95, P = 0.004) were independent prognostic factors for CA. Conclusion: NYHA class, proBNP level, and ENDO LSsys of the LV basal level were independently associated with the survival of patients with CA.

OsUGE1 is directly targeted by OsGRF6 to regulate root hair length in rice.

KEY MESSAGE: Root hairs are required for water and nutrient acquisition in plants. Here, we report a novel mechanism that OsUGE1 is negatively controlled by OsGRF6 to regulate root hair elongation in rice. Root hairs are tubular outgrowths generated by the root epidermal cells. They effectively enlarge the soil-root contact area and play essential roles for nutrient and water absorption. Here, in this study, we demonstrated that the Oryza sativa UDP-glucose 4-epimerase 1-like (OsUGE1) negatively regulated root hair elongation and was directly targeted by Oryza sativa growth regulating factor 6 (OsGRF6). Knockout mutants of OsUGE1 using CRISPR-Cas9 technology showed longer root hairs than those of wild type. In contrast, overexpression lines of OsUGE1 displayed shorter root hair compared with those of wild type. GUS staining showed that it could specifically express in root hair. Subcellular localization analysis indicates that OsUGE1 is located in endoplasmic reticulum, nucleus and plasma membrane. More importantly, ChIP-qPCR, Yeast-one-hybrid and BiFC experiments revealed that OsGRF6 could bind to the promoter of OsUGE1. Furthermore, knockout mutants of OsGRF6 showed shorter root hair than those of wild type, and OsGRF6 dominantly expressed in root. In addition, the expression level of OsUGE1 is significantly downregulated in Osgrf6 mutant. Taken together, our study reveals a novel pathway that OsUGE1 is negatively controlled by OsGRF6 to regulate root hair elongation in rice.

Novel three-dimensional Ti3C2-MXene embedded zirconium alginate aerogel adsorbent for efficient phosphate removal in water.

Excessive phosphorus in water causes environmental security problems like eutrophication. Advanced two-dimensional material MXene has attracted raising attention in aquatic adsorption, while lack of selectivity and difficult recovery limit its application in phosphate removal. In this study, Ti3C2-MXene embedded zirconium-crosslinked SA (MX-ZrSA) beads were synthesized and their phosphate adsorption performance under different conditions was assessed. Investigations using SEM/EDS, XRD, BET, TGA and contact angle meter reveal that the addition of Ti3C2-MXene enhanced the thermal stability, mechanical strength, hydrophilicity, and formed loose network-like mesoporous inner structure with large surface area. The theoretical maximum adsorption capacity was 492.55 mg P/g and was well fitted by Freundlich and optimized Langmuir models. The Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy analysis showed that chemisorption was involved, and the formation of Zr-O-P and Ti-O-P complexes accounted for high selectivity and affinity to phosphate. The adsorption experiments in real waters and lab-scale continuous flow Anaerobic-Anoxic-Oxic reactor further indicated the application potential of MX-ZrSA beads. Our study will provide insight into MXene and SA aerogel synergistic adsorption of aquatic contaminants and help with the removal and recovery of finite phosphorus resource.

Highly selective electrosynthesis of H2O2 by N, O co-doped graphite nanosheets for efficient electro-Fenton degradation of p-nitrophenol.

The activity and selectivity of the cathode towards electrosynthesis of H2O2 are critical for electro-Fenton process. Herein, nickel-foam modified with N, O co-doped graphite nanosheets (NO-GNSs/Ni-F) was developed as a cathode for highly efficient and selective electrosynthesis of H2O2. Expectedly, the accumulation of H2O2 at pH= 3 reached 494.2 mg L-1 h-1, with the selectivity toward H2O2 generation reaching 93.0%. The synergistic effect of different oxygen-containing functional groups and N species on the performance and selectivity of H2O2 electrosynthesis was investigated by density functional theory calculations, and the combination of epoxy and graphitic N (EP + N) was identified as the most favorable configuration with the lowest theoretical overpotential for H2O2 generation. Moreover, NO-GNSs/Ni-F was applied in the electro-Fenton process for p-nitrophenol degradation, resulting in 100% removal within 15 min with the kinetic rate constant of 0.446 min-1 and 97.6% mineralization within 6 h. The efficient removal was mainly attributed to the generation of bulk ·OH. Furthermore, NO-GNSs/Ni-F exhibited excellent stability. This work provides a workable option for the enhancement of H2O2 accumulation and the efficient degradation of pollutants in electro-Fenton system.

Synthesis and Biological Evaluation of Phthalideisoquinoline Derivatives.

A photo and Cu-mediated radical-radical approach enabling the one-step synthesis of the phthalideisoquinoline skeleton has been reported. Under mild reaction conditions, a series of N-aryl phthalideisoquinolines containing various substituents were synthesized in moderate to good yields. Bioactivity data demonstrated that a new compound 4x can efficiently inhibit the growth of multiple tumor cell lines with enhancements of more than 10-fold by significantly increasing G2/M arrest compared with noscapine.

A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection.

Echocardiography is the first-line diagnostic technique for heart diseases. Although artificial intelligence techniques have made great improvements in the analysis of echocardiography, the major limitations remain to be the built neural networks are normally adapted to a few diseases and specific equipment. Here, we present an end-to-end deep learning framework named AIEchoDx that differentiates four common cardiovascular diseases (Atrial Septal Defect, Dilated Cardiomyopathy, Hypertrophic Cardiomyopathy, prior Myocardial Infarction) from normal subjects with performance comparable to that of consensus of three senior cardiologists in AUCs (99.50% vs 99.26%, 98.75% vs 92.75%, 99.57% vs 97.21%, 98.52% vs 84.20%, and 98.70% vs 89.41%), respectively. Meanwhile, AIEchoDx accurately recognizes critical lesion regions of interest along with each disease by visualizing the decision-making process. Furthermore, our analysis indicates that heterogeneous diseases, like dilated cardiomyopathy, could be classified into two phenogroups with distinct clinical characteristics. Finally, AIEchoDx performs efficiently as an anomaly detection tool when applying handheld device-produced videos. Together, AIEchoDx provides a potential diagnostic assistant tool in either cart-based echocardiography equipment or handheld echocardiography device for primary and point-of-care medical personnel with high diagnostic performance, and the application of lesion region identification and heterogeneous disease phenogrouping, which may broaden the application of artificial intelligence in echocardiography.

Neurobehavioral effects of cinnabar and the cinnabar-containing pediatric prescription, Yi-Nian-Jin, in juvenile rats.

BACKGROUND: Cinnabar, a mercury-containing mineral medicine, has long been widely used in pediatric prescriptions. The safety of cinnabar-containing prescriptions, particularly for children, is drawing increasing attention worldwide. However, whether cinnabar and these pediatric prescriptions have adverse effects on neurobehavior is unknown. Yi-Nian-Jin (YNJ), a classic pediatric prescription, contains 5.66% (w/w) cinnabar, along with other four herbs. YNJ is widely prescribed to promote digestion, eliminate phlegm, and prevent constipation in children (aged 0-6 years). In this study, we used YNJ as an example of cinnabar-containing pediatric prescriptions to determine mercury absorption, distribution, and accumulation and further investigate its potential neurotoxicity in juvenile rats. MATERIAL AND METHODS: Low (67.9 mg/kg), middle (169.8 mg/kg), and high dose (339.6 mg/kg) of cinnabar, and low (1.2 g/kg), middle (3.0 g/kg), and high dose (6.0 g/kg) of YNJ were used in this study, corresponding to 3, 7.5, and 15 times the clinically equivalent dose, respectively. Juvenile rats were orally administered different doses of cinnabar or YNJ for 14 consecutive days. The mercury content in rat blood and tissues (brain, liver, and kidney) and serum biochemical changes on day 14 of consecutive administration and on day 14 after cessation were measured. Moreover, a series of behavioral assays (open field, elevated plus-maze, and Morris water maze assays) were performed after 14 consecutive days of administration. RESULTS: The mercury absorption, distribution, and accumulation of cinnabar and YNJ in juvenile rats were substantially different. Mercury in cinnabar was absorbed to a greater extent than that in YNJ, and the mercury content in cinnabar high-dose group (cinnabar-H) was approximately seven times higher than that in YNJ high-dose group (YNJ-H) on day 14 of administration. In contrast, compared with that of cinnabar, the mercury content in YNJ accumulated more in the tissues, especially in the brain and kidney. Repeated administration of cinnabar or YNJ did not affect liver function, renal function, learning, and memory in juvenile rats. However, repeated administration of YNJ at a high dose (6.0 g/kg) affected locomotor activity in juvenile rats. Repeated administration of cinnabar (339.6 mg/kg) or YNJ (>1.2 g/kg) induced anxiety-related behavior in juvenile rats. CONCLUSIONS: Mercury in YNJ exhibited lower absorption but higher accumulation in tissues than those of the mercury in cinnabar. Consecutive oral administration of cinnabar or YNJ had no impact on liver function, renal function, learning, and memory, but could cause motor dysfunction and anxiety in juvenile rats.