Genome-Wide Identification of PAP1 Direct Targets in Regulating Seed Anthocyanin Biosynthesis in Arabidopsis.

Anthocyanins are widespread water-soluble pigments in the plant kingdom. Anthocyanin accumulation is activated by the MYB-bHLH-WD40 (MBW) protein complex. In Arabidopsis, the R2R3-MYB transcription factor PAP1 activates anthocyanin biosynthesis. While prior research primarily focused on seedlings, seeds received limited attention. This study explores PAP1's genome-wide target genes in anthocyanin biosynthesis in seeds. Our findings confirm that PAP1 is a positive regulator of anthocyanin biosynthesis in Arabidopsis seeds. PAP1 significantly increased anthocyanin content in developing and mature seeds in Arabidopsis. Transcriptome analysis at 12 days after pollination reveals the upregulation of numerous genes involved in anthocyanin accumulation in 35S:PAP1 developing seeds. Chromatin immunoprecipitation and dual luciferase reporter assays demonstrate PAP1's direct promotion of ten key genes and indirect upregulation of TT8, TTG1, and eight key genes during seed maturation, thus enhancing seed anthocyanin accumulation. These findings enhance our understanding of PAP1's novel role in regulating anthocyanin accumulation in Arabidopsis seeds.

ELAA: An Ensemble-Learning-Based Adversarial Attack Targeting Image-Classification Model.

The research on image-classification-adversarial attacks is crucial in the realm of artificial intelligence (AI) security. Most of the image-classification-adversarial attack methods are for white-box settings, demanding target model gradients and network architectures, which is less practical when facing real-world cases. However, black-box adversarial attacks immune to the above limitations and reinforcement learning (RL) seem to be a feasible solution to explore an optimized evasion policy. Unfortunately, existing RL-based works perform worse than expected in the attack success rate. In light of these challenges, we propose an ensemble-learning-based adversarial attack (ELAA) targeting image-classification models which aggregate and optimize multiple reinforcement learning (RL) base learners, which further reveals the vulnerabilities of learning-based image-classification models. Experimental results show that the attack success rate for the ensemble model is about 35% higher than for a single model. The attack success rate of ELAA is 15% higher than those of the baseline methods.

Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength.

Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes. Graph neural networks (GNNs) have emerged as a prominent deep learning architecture, capable of efficiently aggregating node information and discerning node influence. However, existing graph neural networks often ignore the strength of the relationships between nodes when aggregating information about neighboring nodes. In complex networks, neighboring nodes often do not have the same influence on the target node, so the existing graph neural network methods are not effective. In addition, the diversity of complex networks also makes it difficult to adapt node features with a single attribute to different types of networks. To address the above problems, the paper constructs node input features using information entropy combined with the node degree value and the average degree of the neighbor, and proposes a simple and effective graph neural network model. The model obtains the strength of the relationships between nodes by considering the degree of neighborhood overlap, and uses this as the basis for message passing, thereby effectively aggregating information about nodes and their neighborhoods. Experiments are conducted on 12 real networks, using the SIR model to verify the effectiveness of the model with the benchmark method. The experimental results show that the model can identify the influence of nodes in complex networks more effectively.

Number-dependent association of potentially inappropriate medications with clinical outcomes and expenditures among community-dwelling older adults: A population-based cohort study.

AIMS: The aim of this study was to investigate the prevalence of potentially inappropriate medication (PIM) prescribing and its number-dependent association (PIM = 1, 2, ≥3) with all-cause hospitalizations, emergency department (ED) visits, and medication expenditures in Beijing, China. METHODS: A retrospective cohort analysis was conducted to analyse PIM prescribing in community-dwelling older adults aged ≥65 years within the Beijing Municipal Medical Insurance Database (data from July to September 2016). The prevalence of PIMs was estimated based on the 2015 Beers Criteria. Logistic models were utilized to investigate the associations between PIM use and all-cause hospitalizations and ED visits. Generalized linear models with the logic link and gamma distribution were used to analyse associations between PIM use and medication expenditures. RESULTS: Among the 506 214 older adults, the prevalence of PIM was 38.07%. After adjusting for covariables, prescribing two and three or more PIMs was associated with increased risks of hospitalizations (PIM = 2: odds ratio [OR] 1.34, 95% confidence interval [CI]: 1.22-1.47; PIM ≥ 3: OR = 1.47, 95% CI: 1.32-1.63) and ED visits (PIM = 2: OR = 1.29, 95% CI 1.12-1.48; PIM ≥ 3: OR = 1.23, 95% CI: 1.04-1.44). Exposures to two and three or more PIMs were associated with higher medication expenditures for inpatient visits (PIM = 2: incidence rate ratio [IRR] = 1.08, 95% CI 1.01-1.16; PIM ≥ 3: IRR = 1.18, 95% CI: 1.08-1.28). Vasodilators were the most frequent PIM prescribing group among patients who were hospitalized or had to visit the ED. CONCLUSIONS: PIMs were prescribed at a high rate among community-dwelling older adults in Beijing. Two or more PIMs were associated with increased risks of hospitalizations, ED visits, and increased inpatient medication expenditures. Effective interventions are needed to target unnecessary and inappropriate medications in older adults.

Length of Stay Among Infants with Neonatal Abstinence Syndrome and Risk of Hospital Readmission.

OBJECTIVES: To assess whether a shorter length of stay (LOS) is associated with a higher risk of readmission among newborns with neonatal abstinence syndrome (NAS) and examine the risk, causes, and characteristics associated with readmissions among newborns with NAS, using a longitudinally linked population-based database. METHODS: Our study sample included full-term singletons with NAS (n = 4,547) and without NAS (n = 327,836), born in Massachusetts during 2011-2017. We used log-binomial regression models to estimate the crude risk ratios (cRRs) and adjusted RRs with 95% confidence intervals (CI) of the association between LOS and readmissions, controlling for maternal age, race/ethnicity, education, marital status, insurance, method of delivery, birthweight, adequacy of prenatal care, smoking, and abnormal conditions of newborn. RESULTS: Compared with infants without NAS, infants with NAS had a non-significantly higher risk of readmission within 2-42 days (2.8% vs. 2.5%; p = 0.17) and a significantly higher risk of readmission within 43-182 days (2.7% vs. 1.8%; p < 0.001). The risk of readmission within 2-42 days was significantly higher among infants with NAS with a LOS of 0-6 days compared to a LOS of 14-20 days (reference group) (aRR: 2.1; 95% CI: 1.2-3.5). No significant differences in readmission rates between 43 and 182 days were observed across LOS categories. CONCLUSIONS: Among infants with NAS, a LOS of 0-6 days was associated with a significantly higher risk of readmission within 2-42 days of discharge compared to a longer LOS.

GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings.

In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task.

Visible light regulates anthocyanin synthesis via malate dehydrogenases and the ethylene signaling pathway in plum (Prunus salicina L.).

Light regulates anthocyanins synthesis in plants. Upon exposure to visible light, the inhibition of photosynthetic electron transfer significantly lowered the contents of anthocyanins and the expression levels of key genes involved in anthocyanins synthesis in plum fruit peel. Meanwhile, the expression levels of PsmMDH2 (encoding the malate dehydrogenase in mitochondria) and PschMDH (encoding the malate dehydrogenase in chloroplasts) decreased significantly. The contents of anthocyanins and the levels of the key genes involved in anthocyanin synthesis decreased significantly with the treatment of 1-MCP (an inhibitor of ethylene perception) but were enhanced by the exogenous application of ethylene. The ethylene treatment could also recover the anthocyanin synthesis capacity lowered by the photosynthetic electron transfer inhibition. Silencing PsmMDH2 and PschMDH significantly lowered the contents of anthocyanins in plum fruit. At low temperature, visible light irradiation induced anthocyanin accumulation in Arabidopsis leaves. However, the mmdh, chmdh, and etr1-1 mutants had significantly lower anthocyanins content and expressions of the key genes involved in anthocyanins synthesis compared to wild type. Overall, the present study demonstrates that both photosynthesis and respiration were involved in the regulation of anthocyanin synthesis in visible light. The visible light regulates anthocyanin synthesis by controlling the malate metabolism via MDHs and the ethylene signaling pathway.

Risk factors for emergency room use and rehospitalization among opioid-exposed newborns in Massachusetts.

OBJECTIVE: To determine the risk factors for receipt of emergency room (ER) care and rehospitalization among opioid-exposed newborns in Massachusetts. DESIGN/METHODS: We analyzed two linked data sets from 2002 to 2010: (a) Massachusetts Pregnancy to Early Life Longitudinal Data System and (b) Massachusetts Bureau of Substance Abuse Services Management Information System. Generalized estimating equations were used to assess the independent association of maternal and infant characteristics with ER use and rehospitalization in the first year of life. RESULTS: Four thousand and five hundred and twenty-four maternal-infant dyads affected by OUD were included in the analysis. In adjusted analysis, risk factors for receipt of ER care included Hispanic ethnicity (aOR 1.63 [95% CI 1.30-2.05]), lower education levels (aOR 1.54-1.69 [95% CI 1.12-2.31]), nonprivate insurance (aOR 1.44 [95% CI 1.11-1.86]), and presence of maternal chronic conditions (aOR 1.14 [95% CI 1.01-1.29]). Risk factors for rehospitalization included prematurity (aOR 1.44 [95% CI 1.14-1.82]), low birthweight (aOR 2.02 [95% CI 1.63-2.49]), and nonprivate insurance (aOR 1.58 [95% CI 1.13-2.22]). Prolonged infant birth hospitalization was protective against both ER use (aOR 0.84 [95% CI 0.73-0.96]) and rehospitalization (aOR 0.63 [95% CI 0.53-0.75]). CONCLUSIONS: Clinical and sociodemographic characteristics are risk factors for receipt of ER care and rehospitalization among opioid-exposed newborns in Massachusetts.

Optimizing the control group for evaluating ART outcomes: can outpatient claims data yield a better control group?

PURPOSE: We previously developed a subfertile comparison group with which to compare outcomes of assisted reproductive technology (ART) treatment. In this study, we evaluated whether insurance claims data in the Massachusetts All Payers Claims Database (APCD) defined a more appropriate comparison group. METHODS: We used Massachusetts vital records of women who delivered between 2013 and 2017 on whom APCD data were available. ART deliveries were those linked to a national ART database. Deliveries were subfertile if fertility treatment was marked on the birth certificate, had prior hospitalization with ICD code for infertility, or prior fertility treatment. An infertile group included women with an APCD outpatient or inpatient ICD 9/10 infertility code prior to delivery. Fertile deliveries were none of the above. Demographics, health risks, and obstetric outcomes were compared among groups. Multivariable generalized estimating equations were used to calculate adjusted relative risk (aRR) and 95% confidence intervals (CI). RESULTS: There were 70,726 fertile, 4,763 subfertile, 11,970 infertile, and 7,689 ART-treated deliveries. Only 3,297 deliveries were identified as both subfertile and infertile. Both subfertile and infertile were older, and had more education, chronic hypertension, and diabetes than the fertile group and less than the ART-treated group. Prematurity (aRR = 1.15-1.17) and birthweight (aRR = 1.10-1.21) were increased in all groups compared with the fertile group. CONCLUSION: Although the APCD allowed identification of more women than the previously defined subfertile categorization and allowed us to remove previously unidentified infertile women from the fertile group, it is not clear that it offered a clinically significantly improved comparison group.

Farnesoid X receptor (FXR) inhibits coagulation process via inducing hepatic antithrombin III expression in mice.

Farnesoid X receptor (FXR) has been identified as an inhibitor of platelet function and an inducer of fibrinogen protein complex. However, the regulatory mechanism of FXR in hemostatic system remains incompletely understood. In this study, we aimed to investigate the functions of FXR in regulating antithrombin III (AT III). C57BL/6 mice and FXR knockout (FXR KO) mice were treated with or without GW4064 (30 mg/kg per day). FXR activation significantly prolonged prothrombin time (PT) and activated partial thromboplastin time (APTT), lowered activity of activated factor X (FXa) and concentrations of thrombin-antithrombin complex (TAT) and activated factor II (FIIa), and increased level of AT III, whereas all of these effects were markedly reversed in FXR KO mice. In vivo, hepatic AT III mRNA and protein expression levels were up-regulated in wild-type mice after FXR activation, but down-regulated in FXR KO mice. In vitro study showed that FXR activation induced, while FXR knockdown inhibited, AT III expression in mouse primary hepatocytes. The luciferase assay and ChIP assay revealed that FXR can bind to the promoter region of AT III gene where FXR activation increased AT III transcription. These results suggest FXR activation inhibits coagulation process via inducing hepatic AT III expression in mice. The present study reveals a new role of FXR in hemostatic homeostasis and indicates that FXR might act as a potential therapeutic target for diseases related to hypercoagulation.

Bert-Enhanced Text Graph Neural Network for Classification.

Text classification is a fundamental research direction, aims to assign tags to text units. Recently, graph neural networks (GNN) have exhibited some excellent properties in textual information processing. Furthermore, the pre-trained language model also realized promising effects in many tasks. However, many text processing methods cannot model a single text unit's structure or ignore the semantic features. To solve these problems and comprehensively utilize the text's structure information and semantic information, we propose a Bert-Enhanced text Graph Neural Network model (BEGNN). For each text, we construct a text graph separately according to the co-occurrence relationship of words and use GNN to extract text features. Moreover, we employ Bert to extract semantic features. The former part can take into account the structural information, and the latter can focus on modeling the semantic information. Finally, we interact and aggregate these two features of different granularity to get a more effective representation. Experiments on standard datasets demonstrate the effectiveness of BEGNN.

Hyperledger Fabric Access Control System for Internet of Things Layer in Blockchain-Based Applications.

Blockchain-based applications are gaining traction in various application fields, including supply chain management, health care, and finance. The Internet of Things (IoT) is a critical component of these applications since it allows for data collection from the environment. In this work, we integrate the Hyperledger Fabric blockchain and IoT devices to demonstrate the access control and establish the root of trust for IoT devices. The Hyperledger Fabric is designed to be secure against unwanted access and use through encryption protocols, access restrictions, and cryptography algorithms. An attribute-based access control (ABAC) mechanism was created using Hyperledger Fabric components only to gain access to the IoT device. Single board computers based on the ARM architecture are becoming increasingly powerful and popular in automation applications. In this study, the Raspberry Pi 4 Model B based on ARM64 architecture is used as the IoT device. Because the ARM64 architecture is not supported by default, we build executable binaries and Docker images for the ARM64 architecture, using the Hyperledger Fabric source code. On an IoT device, we run the fabric node in native mode to evaluate the executable binaries generated for the ARM64 architecture. Through effective chaincode execution and testing, we successfully assess the Hyperledger fabric blockchain implementation and access control mechanism on the ARM64 architecture.

MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion.

Significant progress has been made in generating counterfeit images and videos. Forged videos generated by deepfaking have been widely spread and have caused severe societal impacts, which stir up public concern about automatic deepfake detection technology. Recently, many deepfake detection methods based on forged features have been proposed. Among the popular forged features, textural features are widely used. However, most of the current texture-based detection methods extract textures directly from RGB images, ignoring the mature spectral analysis methods. Therefore, this research proposes a deepfake detection network fusing RGB features and textural information extracted by neural networks and signal processing methods, namely, MFF-Net. Specifically, it consists of four key components: (1) a feature extraction module to further extract textural and frequency information using the Gabor convolution and residual attention blocks; (2) a texture enhancement module to zoom into the subtle textural features in shallow layers; (3) an attention module to force the classifier to focus on the forged part; (4) two instances of feature fusion to firstly fuse textural features from the shallow RGB branch and feature extraction module and then to fuse the textural features and semantic information. Moreover, we further introduce a new diversity loss to force the feature extraction module to learn features of different scales and directions. The experimental results show that MFF-Net has excellent generalization and has achieved state-of-the-art performance on various deepfake datasets.

A Transformer-Based Hierarchical Variational AutoEncoder Combined Hidden Markov Model for Long Text Generation.

The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences. There is a particular relationship between each sentence, especially between the latent variables that control the generation of the sentences. The relationships between these latent variables help in generating continuous and logically connected long texts. There exist very few studies on the relationships between these latent variables. We proposed a method for combining the Transformer-Based Hierarchical Variational AutoEncoder and Hidden Markov Model (HT-HVAE) to learn multiple hierarchical latent variables and their relationships. This application improves long text generation. We use a hierarchical Transformer encoder to encode the long texts in order to obtain better hierarchical information of the long text. HT-HVAE's generation network uses HMM to learn the relationship between latent variables. We also proposed a method for calculating the perplexity for the multiple hierarchical latent variable structure. The experimental results show that our model is more effective in the dataset with strong logic, alleviates the notorious posterior collapse problem, and generates more continuous and logically connected long text.

Early Autism Spectrum Disorders in Children Born to Fertile, Subfertile, and ART-Treated Women.

INTRODUCTION: We examined the prevalence of autism spectrum disorders (ASDs) in Massachusetts (MA) comparing children born via assisted reproductive technology (ART) and children born to women with indicators of subfertility but no ART (Subfertile), to children born to women with neither ART nor indicators of subfertility (Fertile). We assessed the direct, indirect, and total effects of ART and subfertility on ASD among singletons. METHODS: This study included 10,147 ART, 8072 Subfertile and 441,898 Fertile MA resident births from the MA Outcome Study of ART (MOSART) database linked with Early Intervention program participation data. ART included fresh in vitro fertilization (IVF), fresh intracytoplasmic sperm injection (ICSI), and frozen embryo transfer. We estimated the prevalence of ASD by fertility group. We used logistic regression to assess the natural direct effect (NDE), natural indirect effect (NIE) through preterm birth, and total effects of each fertility group on ASD. RESULTS: The NDE indicated that, compared to the Fertile group, the odds of ASD were not statistically higher in the ART (ORNDE 1.07; 95% CI 0.88-1.30), Subfertile (ORNDE 1.11; 95% CI 0.89-1.38), IVF (ORNDE 0.91; 95% CI 0.68-1.22), or ICSI (ORNDE 1.13; 95% CI 0.84-1.51) groups, even if the rate of preterm birth was the same across all groups. The total effect (product of NDE and NIE) was not significant for ART (ORTotal Effect 1.08; 95% CI 0.89-1.30), Subfertile (ORTotal Effect 1.11; 95% CI 0.89-1.38), IVF (ORTotal Effect 0.92; 95% CI 0.69-1.23), or ICSI (ORTotal Effect 1.13; 95% CI 0.84-1.52). CONCLUSION: Compared to children born to Fertile women, children born to ART, ICSI, or IVF, or Subfertile women are not at increased risk of receiving an ASD diagnosis.

Salen-Based Covalent Organic Framework.

The Salen unit represents one of the most important ligands in coordination chemistry. We report herein the first example of a Salen-based covalent organic framework (COF), in which both the construction of the COF structure and the functionalization with Salen moieties have been realized in a single step. Due to its structural uniqueness, the obtained COF material, Salen-COF, possesses high crystallinity and excellent stability. Based on this, a series of metallo-Salen-based COFs were prepared via metalation for further applications.

iTRAQ-based Protein Profiling and Fruit Quality Changes at Different Development Stages of Oriental Melon.

BACKGROUND: Oriental melon is one of the most popular crops for its nutritional and flavour quality. Components that determine melon quality, such as sugar, colour, texture, flavour and aroma, among other factors, accumulate in different developmental stages. Thus, correlating the proteomic profiles with the biochemical and physiological changes occurring in the oriental melon is very important for advancing our understanding of oriental melon quality in the ripening processes. RESULTS: iTRAQ-based protein profiling was conducted on 'YuMeiren' oriental melon fruit at different developmental stages. Physiological quality indices, including firmness, rind colour, soluble solids content (SSC), ethylene production, sugar content and volatile compounds were also characterized during four maturity periods of the melon, including 5, 15, 25 and 35 days after anthesis (DAA). A principal component analysis (PCA) revealed that the aroma volatiles at 5 DAA and 15 DAA were similar and separated from that of 35 DAA. More than 5835 proteins were identified and quantified in the two biological repeats and divided into 4 clusters by hierarchical cluster analysis. A functional analysis was performed using Blast2GO software based on the enrichment of a GO analysis for biological process, molecular function and cellular components. The main KEGG pathways, such as glycolysis, α-linolenic acid and starch and sucrose metabolism, were analyzed. The gene family members corresponding to differentially expressed proteins, including lipoxygenase (CmLOX01-18) and alcohol acetyltransferase (CmAAT1-4) involved in the α-linolenic acid metabolic pathway, were verified with real-time qPCR. The results showed that the expression patterns of 64.7% of the genes were consistent with the expression patterns of the corresponding proteins. CONCLUSIONS: This study combined the variation of the quality index and differentially expressed proteins of oriental melon at different developmental stages that laid the foundation for the subsequent protein and gene function validation.

Maternal Substance Use Disorders and Infant Outcomes in the First Year of Life among Massachusetts Singletons, 2003-2010.

OBJECTIVE: To determine the association of maternal substance use disorders (SUDs) during pregnancy with adverse neonatal outcomes and infant hospital re-admissions, observational stays, and emergency department utilization in the first year of life. STUDY DESIGN: We analyzed 2 linked statewide datasets from 2002 to 2010: the Massachusetts Pregnancy to Early Life Longitudinal data system and the Massachusetts Bureau of Substance Abuse Services Management Information System. Generalized estimating equations were used to assess the association of maternal SUDs and neonatal outcomes and infant hospital-based care in the first year of life, controlling for maternal and infant characteristics. RESULTS: Maternal SUDs increased from 19.4 per 1000 live births in 2003 to 31.1 per 1000 live births in 2009. In the adjusted analysis, exposed neonates were more likely to be born preterm (aOR 1.85; 95% CI 1.75-1.96) and low birthweight (aOR 1.94; 95% CI 1.80-2.09). After controlling for maternal characteristics and preterm birth, SUD-exposed neonates were more likely to have intrauterine growth restriction, cardiac, respiratory, neurologic, infectious, hematologic, and feeding/nutrition problems, prolonged hospital stay, and higher mortality (aOR range 1.26-3.80). Exposed infants were more likely to be rehospitalized (aOR 1.10; 95% CI 1.04-1.17) but less likely to have an observational stay (aOR 0.90; 95% CI 0.82-0.99) or use the emergency department (aOR 0.87; 95% CI 0.83-0.90) in the first year of life. CONCLUSIONS: Infants born to mothers with SUD are at higher risk for adverse health outcomes in the perinatal period and are also more likely to be rehospitalized in the first year of life.

Splicing-related single nucleotide polymorphism of RAB, member of RAS oncogene family like 2B (RABL2B) jeopardises semen quality in Chinese Holstein bulls.

RAB, member of RAS oncogene family like 2B (RABL2B) is a member of a poorly characterised clade of the RAS GTPase superfamily, which plays an essential role in male fertility, sperm intraflagellar transport and tail assembly. In the present study, we identified a novel RABL2B splice variant in bovine testis and spermatozoa. This splice variant, designated RABL2B-TV, is characterised by exon 2 skipping. Moreover, a single nucleotide polymorphism (SNP), namely c.125G>A, was found within the exonic splicing enhancer (ESE) motif, indicating that the SNP caused the production of the RABL2B-TV aberrant splice variant. This was demonstrated by constructing a pSPL3 exon capturing vector with different genotypes and transfecting these vectors into murine Leydig tumour cell line (MLTC-1) cells. Expression of the RABL2B-TV transcript was lower in semen from high- versus low-performance bulls. Association analysis showed that sperm deformity rate was significantly lower in Chinese Holstein bulls with the GG or GA genotype than in bulls with the AA genotype (P<0.05). In addition, initial sperm motility was significantly higher in individuals with the GG or GA genotype than in individuals with the AA genotype (P<0.05). The findings of the present study suggest that the difference in semen quality in bulls with different RABL2B genotypes is generated via an alternative splicing mechanism caused by a functional SNP within the ESE motif.

Mechanisms Underlying Endothelin-1 Level Elevations Caused by Excessive Fluoride Exposure.

OBJECTIVE: To explore the mechanisms underlying endothelin-1 (ET-1) elevations induced by excessive fluoride exposure. METHODS: We measured serum and bone fluoride ion content and plasma ET-1 levels and compared these parameters among different groups in an animal model. We also observed morphological changes in the aorta and endothelium of rabbits. In cell experiments, human umbilical vein endothelial cells (HUVECs) were treated with varying concentrations of NaF for 24h, with or without 10 µM U0126 pretreatment for 1 h. ET-1 levels in culture fluid and intracellular reactive oxygen species (ROS) levels, as well as ET1 gene, endothelin-converting enzyme-1 (ECE-1), extracellular signal-regulating kinase 1/2 (ERK1/2), pERK1/2 expression levels and RAS activation were measured and compared among the groups. RESULTS: Plasma ET-1 levels of rabbits increased significantly in fluorinated groups compared with those in the control group. The rabbit thoracic aortas became slightly hardened in fluorinated groups compared with those in the control group, and some vacuoles were present in the endothelial cell cytoplasm of the rabbits in fluorinated groups. In our cell experiments, ET1 gene and ECE-1 expression levels in HUVECs and ET-1 expression levels in the cell culture supernatants increased significantly in some experimental groups compared with those in the control group. These trends paralleled the changes in intracellular ROS levels, RAS activation, and the pERK1/2-to-ERK1/2 ratio. After U0126 was added, ECE-1 expression and ET-1 levels decreased significantly. CONCLUSION: Excessive fluoride exposure leads to characteristic endothelial damage (vacuoles), thoracic aorta hardening, and plasma ET-1 level elevations in rabbits. In addition, the ROS-RAS-MEK1/2-pERK1/2/ERK1/2 pathway plays a crucial-and at least partial-role in ET-1 over-expression, which is promoted by excessive fluoride exposure.