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Abstract How to utilize modern technological means to provide both accurate scoring and objective feedback for martial arts movements has become an issue that needs to be addressed in the field of physical education. This study proposes an intelligent scoring method based on machine learning. Firstly, the key features are extracted by the feature alignment technique, which eliminates the influence of athletes’ movement speed, rhythm and duration on the scoring, thus reflecting the athletes’ skill level more realistically. Second, to further improve the objectivity and accuracy, an adaptive weighted multi-model decision-making strategy is proposed. In addition, this study is the first to use interpretable artificial intelligence to provide feedback for teaching and learning Wushu. Experimental results indicate that the integrated model using the weighted average strategy not only outperforms other algorithms after feature alignment (on the XSQ dataset MAE is 0.237, RMSE is 0.442, sMAPE is 8.569, R $$^{2}$$ is 0.633, Pearson’s correlation is 0.807, ICC is 0.856. on the UMONS-Taichi dataset, MAE is 0.29, RMSE is 0.438, sMAPE is 10.01, R $$^{2}$$ is 0.844, Pearson correlation is 0.921, and ICC is 0.954. on the PBB dataset, MAE is 0.261, RMSE is 0.351, sMAPE is 6.765, R $$^{2}$$ is 0.557, Pearson correlation is 0.753, and ICC is 0.82), but is also close to the performance of human experts. In conclusion, this study not only achieves a performance of movement evaluation comparable to human experts, but also provides a technical framework for the rapid realizing of automatic scoring in other martial arts styles, which will promote the popularization and development of martial arts education.

Abstract Background Pumpkin (Cucurbita moschata) is an important vegetable crop that often suffers from low-temperature stress during growth. However, the molecular mechanism involved in its response to chilling stress remains unknown. In this study, we comprehensively investigated the effect of chilling stress in pumpkin seedlings by conducting physiological, transcriptomic, and metabolomic analyses. Results Under chilling stress, there was an overall increase in relative electrical conductivity, along with malondialdehyde, soluble sugar, and soluble protein contents, but decreased superoxide dismutase and peroxidase activities and chlorophyll contents in seedling leaves compared with controls. Overall, 5,780 differentially expressed genes (DEGs) and 178 differentially expressed metabolites (DEMs) were identified under chilling stress. Most DEGs were involved in plant hormone signal transduction and the phenylpropanoid biosynthesis pathway, and ERF, bHLH, WRKY, MYB, and HSF transcription factors were induced. Metabolomic analysis revealed that the contents of salicylic acid (SA), phenylalanine, and tyrosine increased in response to chilling stress. The findings indicated that the SA signaling and phenylpropanoid biosynthesis pathways are key to regulating the responses to chilling stress in pumpkins. Conclusion Overall, our study provides valuable insights into the comprehensive response of C. moschata to chilling stress, enriching the theoretical basis of this mechanism and facilitating the development of molecular breeding strategies for pumpkin tolerance to chilling stress.

IntroductionBASIC PENTACYSTEINE (BPC) is a small transcription factor family known for its role in various developmental processes in plants, particularly in binding GA motifs and regulating flower and seed development. However, research on the functional characteristics and target genes of BPCs in coconut (Cocos nucifera) is limited.MethodsIn this study, we systematically characterized the gene structure, conserved protein domains, gene expansion, and target genes of CnBPCs in the coconut genome. We conducted yeast one-hybrid (Y1H) and dual-luciferase assay to explore gene interactions. We identified genes with the GA motif in their promoter regions and combined this information with a weighted gene co-expression network to identify the target genes of CnBPCs.ResultsEight CnBPCs were identified, including three Class I CnBPCs from triplication, four Class II CnBPCs (with CnBPC6A and CnBPC6B resulting from segmental duplication), and one Class III CnBPC (CnBPC7). Three conserved DNA-binding motifs were detected, exhibiting variation in certain sites. Widespread BPC gene expansion was detected in coconut and other plant species, while only three BPCs were found in the most basal extant flowering plant. Notably, 92% of protein-coding genes contained at least one GA motif, with the (GA)3 motif being most prevalent. Genes containing the GA motif that exhibit a high expression correlation with CnBPCs, tend to interact strongly with the corresponding CnBPCs. Additionally, promoters rich in the GA motif tend to interact with all members of CnBPC. The dual-luciferase assay showed that CnBPCs could activate or repress the transcriptional activities of promoters containing either (GA)3 or (GA)11 motif but with a bias toward certain genes. Furthermore, we constructed co-expressed networks identifying 426 genes with GA motifs as potential CnBPC targets.DiscussionOur findings suggest that CnBPCs may play significant roles in seed germination, flower development, and mesocarp development by interacting with genes such as CnAG1, CnAG2, CnSTK, CnMFT, and CnCS. This study characterized CnBPCs’ binding motif and possible target genes, laying a theoretical foundation to reveal CnBPCs’ function in flower and seed development.

AbstractAcute myocardial infarction, characterized by high morbidity and mortality, has now become a serious health hazard for human beings. Conventional surgical interventions to restore blood flow can rapidly relieve acute myocardial ischemia, but the ensuing myocardial ischemia-reperfusion injury (MI/RI) and subsequent heart failure have become medical challenges that researchers have been trying to overcome. The pathogenesis of MI/RI involves several mechanisms, including overproduction of reactive oxygen species, abnormal mitochondrial function, calcium overload, and other factors that induce cell death and inflammatory responses. These mechanisms have led to the exploration of antioxidant and inflammation-modulating therapies, as well as the development of myocardial protective factors and stem cell therapies. However, the short half-life, low bioavailability, and lack of targeting of these drugs that modulate these pathological mechanisms, combined with liver and spleen sequestration and continuous washout of blood flow from myocardial sites, severely compromise the expected efficacy of clinical drugs. To address these issues, employing conventional nanocarriers and integrating them with contemporary biomimetic nanocarriers, which rely on passive targeting and active targeting through precise modifications, can effectively prolong the duration of therapeutic agents within the body, enhance their bioavailability, and augment their retention at the injured myocardium. Consequently, these approaches significantly enhance therapeutic effectiveness while minimizing toxic side effects. This article reviews current drug delivery systems used for MI/RI, aiming to offer a fresh perspective on treating this disease.

Segmenting plant organs is a crucial step in extracting plant phenotypes. Despite the advancements in point-based neural networks, the field of plant point cloud segmentation suffers from a lack of adequate datasets. In this study, we addressed this issue by generating Arabidopsis models using L-system and proposing the surface-weighted sampling method. This approach enables automated point sampling and annotation, resulting in fully annotated point clouds. To create the Arabidopsis dataset, we employed Voxel Centroid Sampling and Random Sampling as point cloud downsampling methods, effectively reducing the number of points. To enhance the efficiency of semantic segmentation in plant point clouds, we introduced the Plant Stratified Transformer. This network is an improved version of the Stratified Transformer, incorporating the Fast Downsample Layer. Our improved network underwent training and testing on our dataset, and we compared its performance with PointNet++, PAConv, and the original Stratified Transformer network. For semantic segmentation, our improved network achieved mean Precision, Recall, F1-score and IoU of 84.20, 83.03, 83.61 and 73.11%, respectively. It outperformed PointNet++ and PAConv and performed similarly to the original network. Regarding efficiency, the training time and inference time were 714.3 and 597.9 ms, respectively, which were reduced by 320.9 and 271.8 ms, respectively, compared to the original network. The improved network significantly accelerated the speed of feeding point clouds into the network while maintaining segmentation performance. We demonstrated the potential of virtual plants and deep learning methods in rapidly extracting plant phenotypes, contributing to the advancement of plant phenotype research.

Adopting drought-tolerant (DT) cultivars is an effective strategy to sustain maize (Zea mays L.) production under water shortage. Optimizing plant density is an important management practice for improving maize yield. In a two-year field trial, the response of yield, actual evapotranspiration (ETc act), and water productivity (WP) to plant density (6, 7.5, 9 plants m−2) was assessed under irrigated and rainfed conditions using a DT (ZD958) and a drought-susceptible (DS, ZY309) maize cultivar, and additionally, the comparison of soil water depletion will be conducted among soils growing different DT maize varieties. Under rainfed, average yield, ETc act, and WP were 24.7%, 8.6% and 14.8% greater in ZD958 than ZY309, respectively. When density increased from 6 to 9 plants m−2, for ZD958 and ZY309 ETc act remained relatively constant, whereas their yield and WP first increased and then decreased and ultimately reached their maximum at 7.5 plants m−2. Under irrigation, increasing density (6–9 plants m−2) significantly increased yield and WP for ZD958, but for ZY309, yield and WP were not significantly impacted. Yield across seasons did not differ between cultivars at 6 and 7.5 plants m−2, and ZD958 had a 10.2% yield advantage over ZY309 at 9 plants m−2. The findings imply that DT cultivar showed greater high density tolerance than DS cultivar and thus higher optimal density under irrigation. Under rainfed, both cultivars had similar density tolerance and optimum density, whereas DT cultivar had stronger drought tolerance than DS cultivar, which could explain DT cultivar’s greater yield and WP. This study indicate that DT cultivar showed higher and more stable yields than DS cultivar across rainfed and irrigated conditions when grown at optimal densities. Thus, sustainable maize production could be achieved by adopting DT cultivars and optimizing density for different conditions in the study region.

Astroviruses are single-stranded, positive-sense RNA viruses capable of infecting humans as well as a wide range of mammalian and avian species, with a length of approximately 6.6–7.7 kb. In this study, 139 goat fecal samples collected from the Guangxi province were used for the RT-PCR detection, and two of these were positive for goat astrovirus, with a positivity rate of 1.44% (2/139). The complete genome sequence of an astrovirus strain and the partial genome sequence of a strain astrovirus, named GX WZ 2023 and GX HC 2023, were amplified and sequenced, and their sequence lengths were 6284 nt and 6213 nt, respectively. Among them, the capsid protein of goat astrovirus GX HC 2023 showed the highest amino acid identity of 95.9% with ovine astrovirus GX, which belonged to the MAstV-2 genotype. However, the closest relative of the GX WZ 2023 strain was found to be the caprine astrovirus Sichuan, with a nucleotide sequence identity of 76.8%. The ORF1ab nonstructural protein of this strain showed the highest amino acid identities of 89.2 and 95.8% with the ovine astrovirus S5.1 and caprine astrovirus G5.1 strains, respectively. However, its ORF2 capsid protein has 68.4% amino acid identity with the bovine astrovirus (BAstV) 16 2021 CHN strain and only 21.9–64% amino acid identity with all available strains of goat astrovirus. The GX WZ 2023 strain was recombined with the Chinese (BAstV 16 2021 CHN) and Japanese bovine strains (BAstV JPN 2015) in the ORF2 region. Therefore, the goat astrovirus GX WZ 2023 is proposed as a new member of the family goat astroviridae based on the species classification criteria of the International Committee on Taxonomy of Viruses. These findings enhance our understanding of the prevalence and genetic evolution of goat astrovirus and provide a scientific basis for future studies of these viruses in other animals.

Abstract Background Heterosis is widely used in agriculture. However, its molecular mechanisms are still unclear in plants. Here, we develop, sequence, and record the phenotypes of 418 hybrids from crosses between two testers and 265 rice varieties from a mini-core collection. Results Phenotypic analysis shows that heterosis is dependent on genetic backgrounds and environments. By genome-wide association study of 418 hybrids and their parents, we find that nonadditive QTLs are the main genetic contributors to heterosis. We show that nonadditive QTLs are more sensitive to the genetic background and environment than additive ones. Further simulations and experimental analysis support a novel mechanism, homo-insufficiency under insufficient background (HoIIB), underlying heterosis. We propose heterosis in most cases is not due to heterozygote advantage but homozygote disadvantage under the insufficient genetic background. Conclusion The HoIIB model elucidates that genetic background insufficiency is the intrinsic mechanism of background dependence, and also the core mechanism of nonadditive effects and heterosis. This model can explain most known hypotheses and phenomena about heterosis, and thus provides a novel theory for hybrid rice breeding in future.

Abstract Background Rice is the world’s second largest food crop and accelerated global climate change due to the intensification of human activities has a huge impact on rice. Research on the evolution of different rice ecotypes is essential for enhancing the adaptation of rice to the unpredictable environments. Results The sequencing data of 868 cultivated and 140 wild rice accessions were used to study the domestication history and signatures of adaptation in the distinct rice ecotypes genome. The different populations had formed distinct rice ecotypes by phylogenetic analyses and were domesticated independently in the two subspecies of rice, especially deepwater and upland rice. The domestication history of distinct rice ecotypes was confirmed and the four predicted admixture events mainly involved gene flow between wild rice and cultivated rice. Importantly, we identified numerous selective sweeps that have occurred during the domestication of different rice ecotypes and one candidate gene (LOC_Os11g21804) for deepwater based on transcriptomic evidence. In addition, many regions of genomic differentiation between the different rice ecotypes were identified. Furthermore, the main reason for the increase in genetic diversity in the ecotypes of xian (indica) rice was the high proportion of alternative allele frequency in new mutations. Genome-wide association analysis revealed 28 QTLs associated with flood tolerance which contained 12 related cloned genes, and 20 candidate genes within 13 deepwater QTLs were identified by transcriptomic and haplotype analyses. Conclusions These results enhanced our understanding of domestication history in different rice ecotypes and provided valuable insights for genetic improvement and breeding of rice in the current changing environments.

Abstract Background Accurate segmentation of unruptured cerebral aneurysms (UCAs) is essential to treatment planning and rupture risk assessment. Currently, three-dimensional time-of-flight magnetic resonance angiography (3D TOF-MRA) has been the most commonly used method for screening aneurysms due to its noninvasiveness. The methods based on deep learning technologies can assist radiologists in achieving accurate and reliable analysis of the size and shape of aneurysms, which may be helpful in rupture risk prediction models. However, the existing methods did not accomplish accurate segmentation of cerebral aneurysms in 3D TOF-MRA. Methods This paper proposed a CCDU-Net for segmenting UCAs of 3D TOF-MRA images. The CCDU-Net was a cascade of a convolutional neural network for coarse segmentation and the proposed DU-Net for fine segmentation. Especially, the dual-channel inputs of DU-Net were composed of the vessel image and its contour image which can augment the vascular morphological information. Furthermore, a newly designed weighted loss function was used in the training process of DU-Net to promote the segmentation performance. Results A total of 270 patients with UCAs were enrolled in this study. The images were divided into the training (N = 174), validation (N = 43), and testing (N = 53) cohorts. The CCDU-Net achieved a dice similarity coefficient (DSC) of 0.616 ± 0.167, Hausdorff distance (HD) of 5.686 ± 7.020 mm, and volumetric similarity (VS) of 0.752 ± 0.226 in the testing cohort. Compared with the existing best method, the DSC and VS increased by 18% and 5%, respectively, while the HD decreased by one-tenth. Conclusions We proposed a CCDU-Net for segmenting UCAs in 3D TOF-MRA, and the obtained results show that the proposed method outperformed other existing methods.

IntroductionThe flower buds of Lonicera japonica Thunb. are widely used in Chinese medicine for their anti-inflammatory properties, and they have played an important role in the fight against SARS COVID-19 and other major epidemics. However, due to the lack of scientific and accurate variety identification methods and national unified standards, scattered and non-standardized management in flower bud production has led to mixed varieties that have caused significant difficulties in the cataloging and preservation of germplasm resources and the identification, promotion, and application of new L. japonica varieties.MethodsIn this study, we evaluated the population structure, genetic relationships, and genetic fingerprints of 39 germplasm resources of Lonicera in China using simplified genome sequencing technology.ResultsA total of 13,143,268 single nucleotide polymorphisms (SNPs) were identified. Thirty-nine samples of Lonicera were divided into four subgroups, and the population structure and genetic relationships among existing Lonicera germplasm resources were determined using principal component analysis, population structure analysis, and phylogenetic tree analysis. Through several stringent selection criteria, 15 additional streamlined, high-quality DNA fingerprints were filtered out of the validated 50 SNP loci and verified as being able to effectively identify the 39 Lonicera varieties.DiscussionTo our knowledge, this is the first comprehensive study measuring the diversity and population structure of a large collection of Lonicera varieties in China. These results have greatly broadened our understanding of the diversity, phylogeny, and population structure of Lonicera. The results may enhance the future analysis of genetic diversity, species identification, property rights disputes, and molecular breeding by providing a scientific basis and reference data for these efforts.

Grain of indica rice accumulates more toxic cadmium (Cd) than japonica, but the underlying genetic basis is unclear. Here, the authors show that natural variation of OsCd1 contributes to divergence in grain Cd accumulation and transferring japonica allele to indica rice leads to reduced Cd accumulation.

Background: Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1. Methods: This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance. Findings: Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted. Interpretation: The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available.

Cucurbita maxima belong to the genus Cucurbita and are of nutritional and economic importance. Physiological activity, transcriptome, and metabolome analyses of leaf samples from the C. maxima inbreding line IL7 treated at 5 °C and 25 °C were performed. Cold stress resulted in a significant increase in the malondialdehyde content, relative electrical conductivity, soluble protein, sugar content, and catalase activity. A total of 5,553 differentially expressed genes were identified, of which 2,871 were up-regulated and 2,682 down-regulated. In addition, the transcription of differentially expressed genes in the plant hormone signal transduction pathway and transcription factor families of AP2/ERF, bHLH, WRKY, MYB, and HSF was activated. Moreover, 114 differentially expressed metabolites were identified by gas chromatography time-of-flight mass spectrometry, particularly through the analysis of carboxylic acids and derivatives, and organooxygen compounds. The demonstration of a series of potential metabolites and corresponding genes highlighted a comprehensive regulatory mechanism. These findings will provide novel insights into the molecular mechanisms associated with the response to cold stress in C. maxima.

Background: Radiation-induced xerostomia is one of the most common symptoms experienced by cancer patients. The aim of our study is to evaluate the preventive and therapeutic effect of acupuncture for radiation-induced xerostomia in cancer patients. Methods: Eight databases were searched for all published randomized clinical trials (RCTs) on acupuncture for radiation-induced xerostomia in cancer patients up to December 31, 2019. Manual searching included other conference abstracts and reference lists. Meta-analysis was conducted using Revman V.5.3, and risks of bias for included studies was assessed following the Cochrane Handbook. Results: Eight clinical trials (725 participants) were analyzed, and 3 were included in a meta-analysis. All included trials had a high risk of bias, such as selection, performance, and detection bias. Analysis indicated favorable effects of acupuncture regarding the improvement of xerostomia symptoms (MD −3.05, P = 0.02, 95% CI −5.58 to −0.52), compared with sham acupuncture. There were no significant differences between real acupuncture and sham acupuncture regarding the stimulated salivary flow rate (MD 0.37, P = 0.08, 95% CI −0.05 to 0.79) and unstimulated salivary flow rate (MD 0.09, P = 0.12, 95% CI −0.02 to 0.21), which were whole salivary flow rate. Compared with no acupuncture (standard oral care, usual care, or no treatment), acupuncture produced a significant improvement in patient-reported xerostomia, without causing serious adverse effects. However, a Grading of Recommended Assessments analysis revealed that the quality of all acupuncture outcome measures was low. Conclusion: The present meta-analysis and systematic review suggests that acupuncture is effective at improving xerostomia symptoms in cancer patients but not at objective salivary flow measurements. The evidence is still limited due to the low quality of the published studies.

Introduction There is a growing number of randomised controlled trials (RCTs) that focus on functional changes in the brain detected by functional MRI (fMRI) and gut microbiota composition changes after using probiotics.However, the effect of probiotics on functional changes in the brain through gut microbiota remains controversial in existing RCTs. Furthermore, to our knowledge, there is no systematic review to evaluate the effect of probiotics on functional changes in the brain through gut microbiota. Therefore, we aim to summarise literatures evaluating the potential association between probiotics, gut microbiota and functional changes in the brain to elucidate whether probiotics influence gut microbiota and affect functional changes in the brain through gut microbiota.Methods and analysis China National Knowledge Infrastructure, Wanfang Data, VIP Databases (the Chongqing VIP Chinese Science and Technology Periodical Database), SinoMed, PubMed, Web of Science, MEDLINE (The National Library of Medicine), EMBASE (Excerpt Medica Database), Scopus, the Cochrane Central Register of Controlled Trials and ClinicalTrials.gov will be searched until July 2019. The Grey Literature in Europe (OpenSIGLE) database and Google search engine will also be used. The reference lists of each included study will be reviewed to determine whether there are any further relevant studies. RCTs using probiotics compared with a placebo/control will be included. We will use risk of bias assessment and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to assess the quality of evidence. The results of the systematic review will be synthesised narratively in the domains of the three primary outcome measures: (1) Increased/decreased activity in brain regions or altered functional connectivity (FC) of brain detected by fMRI and their association with changes in behaviour, gastrointestinal/emotional symptoms after using probiotics. (2) Changes in composition and diversity of the gut microbiota and their association with changes in behaviour, gastrointestinal/emotional symptoms after using probiotics. (3) Increased/decreased activity in brain regions or altered FC of brain detected by fMRI and the changes in composition or diversity of the gut microbiota after administration of probiotics.Ethics and dissemination The results will be disseminated through a peer-reviewed publication. As no private and confidential patient data will be included in the reporting, there are no ethical considerations associated with this protocol.PROSPERO registration number CRD42019145114.

Introduction Xerostomia is a common symptom in patients with cancer. Currently available methods to manage xerostomia include stringent oral hygiene using fluoride agents and antimicrobials, saliva substitutes and sialagogic agents, but side effects such as headache, dizziness and sweating can occur with these therapies. Clinical trials have shown that acupuncture may be effective in treating xerostomia. The objective of this systematic review is to assess the effectiveness and safety of acupuncture treatment for xerostomia caused by cancer.Methods and analysis This systematic review will incorporate articles identified by electronically searching the following databases: PubMed, MEDLINE, the Cochrane Library, AMED, EMbase, WorldSciNet, Nature, Science Online, China National Knowledge Infrastructure, the Chongqing VIP Chinese Science and Technology Periodical Database, the Wanfang Database and China Biology Medicine Disc from inception to 1 December 2019. Other sources including conference proceedings and reference lists of identified publications and existing systematic reviews will also be searched. Two reviewers will independently search the databases, perform data extraction and assess the quality of studies. Data will be synthesised using either a fixed-effects model or a random-effects model, according to heterogeneity testing. Patient-reported change in the Visual Analogue Scale or the Xerostomia Inventory will be assessed as the primary outcome. Saliva collection, whole saliva production and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 will be evaluated as secondary outcomes. RevMan V. 5.3 will be employed for data analysis. The results will be expressed as risk ratios for dichotomous data and mean differences for continuous data.Ethics and dissemination This protocol will not evaluate individual patient information or affect patient rights and therefore does not require ethical approval. Results from this review will be disseminated through peer-reviewed journals and conference reports.Trial registration number CRD42019129069.

Avoidance of disadvantageous genetic correlations among growth duration and yield traits is critical in developing crop varieties that efficiently use light and energy resources and produce high yields. To understand the genetic basis underlying the correlations among heading date and three major yield traits in rice, we investigated the four traits in a diverse and representative core collection of 266 cultivated rice accessions in both long-day and short-day environments, and conducted the genome-wide association study using 4.6 million single nucleotide polymorphisms (SNPs). There were clear positive correlation between heading date and grain number per panicle, and negative correlation between grain number per panicle and panicle number, as well as different degrees of correlations among other traits in different subspecies and environments. We detected 47 pleiotropic genes in 15 pleiotropic quantitative trait loci (pQTLs), 18 pleiotropic genes containing 37 pleiotropic SNPs in 8 pQTLs, 27 pQTLs with r2 of linkage disequilibrium higher than 0.2, and 39 pairs of interactive genes from 8 metabolic pathways that may contribute to the above phenotypic correlations, but these genetic bases were different for correlations among different traits. Distributions of haplotypes revealed that selection for pleiotropic genes or interactive genes controlling different traits focused on genotypes with weak effect or on those balancing two traits that maximized production but sometimes their utilization strategies depend on the traits and environment. Detection of pQTLs and interactive genes and associated molecular markers will provide an ability to overcome disadvantageous correlations and to utilize the advantageous correlations among traits through marker-assisted selection in breeding.

Dissolved oxygen (DO) is a key factor that influences the healthy growth of fishes in aquaculture. The DO content changes with the aquatic environment and should therefore be monitored online. However, traditional measurement methods, such as iodometry and other chemical analysis methods, are not suitable for online monitoring. The Clark method is not stable enough for extended periods of monitoring. To solve these problems, this paper proposes an intelligent DO measurement method based on the fluorescence quenching mechanism. The measurement system is composed of fluorescent quenching detection, signal conditioning, intelligent processing, and power supply modules. The optical probe adopts the fluorescent quenching mechanism to detect the DO content and solves the problem, whereas traditional chemical methods are easily influenced by the environment. The optical probe contains a thermistor and dual excitation sources to isolate visible parasitic light and execute a compensation strategy. The intelligent processing module adopts the IEEE 1451.2 standard and realizes intelligent compensation. Experimental results show that the optical measurement method is stable, accurate, and suitable for online DO monitoring in aquaculture applications.

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Based on the inhibition of mitochondrial permeability transition pore (mPTP) opening, puerarin (PUE) has a good potential to reduce myocardial ischemia/reperfusion injury (MI/RI). However, the lack of targeting of free PUE makes it difficult to reach the mitochondria. In this paper, we constructed matrix metalloproteinase-targeting peptide (MMP-TP) and triphenylphosphonium (TPP) cation co-modified liposomes loaded with PUE (PUE@T/M-L) for mitochondria-targeted drug delivery. PUE@T/M-L had a favorable particle size of 144.9 ± 0.8 nm, an encapsulation efficiency of 78.9 ± 0.6%, and a sustainedrelease behavior. The results of cytofluorimetric experiments showed that MMP-TP and TPP doublemodified liposomes (T/M-L) enhanced intracellular uptake, escaped lysosomal capture, and promoted drug targeting into mitochondria. In addition, PUE@T/M-L enhanced the viability of hypoxia-reoxygenation (H/R) injured H9c2 cells by inhibiting mPTP opening and reactive oxygen species (ROS) production, reducing Bax expression and increasing Bcl-2 expression. It was inferred that PUE@T/M-L delivered PUE into the mitochondria of H/R injured H9c2 cells, resulting in a significant increase in cellular potency. Based on the ability of MMP-TP to bind the elevated expression of matrix metalloproteinases (MMPs), T/M-L had excellent tropism for Lipopolysaccharide (LPS) -stimulated macrophages and can significantly reduce TNF-a and ROS levels, thus allowing both drug accumulation in ischemic cardiomyocytes and reducing inflammatory stimulation during MI/RI. Fluorescence imaging results of the targeting effect using a DiR probe also indicated that DiR@T/M-L could accumulate and retain in the ischemic myocardium. Taken together, these results demonstrated the promising application of PUE@T/M-L for mitochondria-targeted drug delivery to achieve maximum therapeutic efficacy of PUE. [ABSTRACT FROM AUTHOR]