Traceability Analysis Research Articles

Shenmai injection revives cardiac function in rats with hypertensive heart failure: involvement of microbial-host co-metabolism

Heart failure (HF) is a complex syndrome marked by considerable expenditures and elevated mortality and morbidity rates globally. Shenmai injection (SMI), a form of Traditional Chinese Medicine-based therapy, has demonstrated effectiveness in treating HF. Recent research suggests that Traditional Chinese Medicine (TCM) may induce beneficial changes in microbial-host co-metabolism, potentially providing cardiovascular protection. This study used a rat model of hypertensive heart failure (H-HF) to explore the mechanism of SMI. The possible compounds and key targets of SMI against H-HF were investigated using network pharmacology. The pharmacodynamics of SMI were validated using the H-HF animal model, with analysis of fecal gut microbiota integrating metabolomics and 16S rRNA sequencing. Metorigin metabolite traceability analysis and the MetaboAnalyst platform were utilized to explore the action mechanism. To evaluate changes in serum TMAO levels, targeted metabolomics was performed. Finally, the study looked at the intrinsic relationships among modifications in the intestinal flora, metabolite profile changes, and the targets of SMI compounds to clarify how they might be used to treat H-HF. According to metabolomics and 16S rRNA sequencing, by reestablishing homeostasis in the gut microbiota, SMI affects vital metabolic pathways, such as energy metabolism, amino acid metabolism, and bile acid metabolism. Increased serum TMAO levels were identified to be a risk factor for H-HF, and SMI was able to downregulate the levels of TMAO-related metabolites. Network pharmacology analysis identified 13 active components of SMI targeting 46 proteins, resulting in differential expression changes in 8 metabolites and 24 gut microbes. In conclusion, this study highlights the effectiveness of SMI in alleviating H-HF and its potential to modulate microbial-host co-metabolism. Through a comprehensive discussion of the interconnected relationships among the components, targets, metabolites, and gut microbiota, it provided fresh light on the therapeutic mechanism of SMI on H-HF.

A data-driven framework to identify influencing factors for soil heavy metal contaminations using random forest and bivariate local Moran's I: A case study.

The efficacy of traceability analysis is often limited by a lack of information on influencing factors for heavy metal (HM) contaminations in soil, such as spatial correlations between HM concentrations and influencing factors. To overcome this limitation, a novel data-driven framework was established to identify influencing factors for soil HM concentrations in an industrialised study area, in Guangdong Province, China, mainly using random forest (RF) and bivariate local Moran's I (BLMI) on the basis of the 577 soil samples and the 18 environmental covariates. The quantitative contributions of the 18 influencing factors for the Cd, As, Pb, and Cr concentrations were determined by the optimised RF. The main influencing factors of Cd were petrol stations (10.97%) and railways (9.99%), the main ones of As were groundwater depth (8.45%) and elevation (8.24%), the main ones of Pb were soil pH (8.82%) and hazardous waste disposal sites (8.02%), and the main ones of Cr were mine tailings (13.65%) and rainfall (11.88%). The eight spatial clustering maps between the four HM concentrations and the two key influencing factors were generated by BLMI. The middle part of the study area has shown the higher concentrations of Cd, As, Pb, and Cr, the more complex human activities and the more high-high clusters. Priority attention should be paid to the middle part when taking the specific prevention and control measures for their contaminations. This data-driven framework provided rich information on influencing factors, including HM concentrations, HM contaminations, quantitative contributions, and qualitative spatial clusters.

The Microbial Diversity and Traceability Analysis of Raw Milk from Buffalo Farms at Different Management Ranks in Guangxi Province.

Farm management has a significant impact on microbial composition and may affect the quality of raw buffalo milk. This study involved a diversity analysis and traceability of the microbial communities in raw buffalo milk from three buffalo farms at different management ranks in Guangxi Province, China. The microbial composition of the raw buffalo milk and its environmental sources were investigated using 16S rRNA gene sequencing and bioinformatics analysis. The results demonstrated that different management ranks significantly influenced microbial composition in milk, with the primary sources of contamination varying across farms. The env.OPS_17 was the predominant differential bacterium in farm rank A, whereas Enterobacteriaceae, Aerococcaceae, and Planococcaceae were dominant in farm rank B. The Fast Expectation-Maximization for Microbial Source Tracking model revealed that while the sources of microbial contamination varied across farms at different management ranks, the teat and teat liner consistently emerged as the primary sources of microbial contamination in raw buffalo milk. This study provides important insights into how different farm management ranks affect the microbial composition of raw buffalo milk, highlighting the importance of improved management practices during milk production, particularly in cleaning the milking equipment and farm environment, as these are key factors in ensuring the quality and safety of raw buffalo milk.

Multimodel-based quantitative source apportionment and risk assessment of soil heavy metals: A reliable method to achieve regional pollution traceability and management

To strengthen the control of pollution sources and promote soil pollution management of agricultural land, this study constructed a comprehensive source apportionment framework, which significantly improved the reliability of potential source analysis compared with the traditional single model. The spatial distribution pattern of agricultural soil heavy metals (SHMs) content in Lintong, a typical river valley city in China, was determined and the degree of contamination was evaluated. A scientific source apportionment methodological framework was constructed through correlation analysis methods together with multiple source apportionment receptor models. Finally, the Monte Carlo simulation method was used to derive the results of the human health risk assessment (HHRA). The results revealed the following: (1) Agricultural soils were moderately and mildly polluted, accounting for 28.8 % and 71.2 % of the total number of sampling points, respectively. (2) The overall correlation of heavy metals (HMs) was strong according to the coupling analysis of the SHMs, in which a strong correlation (0.8–1) was reached among Cu, Ni, Pb, Cr and Zn, indicating that these HMs were most likely homologous or composite. (3) Multimodel analysis of the SHMs sources revealed that the first and second principal components were agricultural (41.36 %) and industrial (19.69 %) sources, respectively, and the remaining principal components were road traffic, natural factors, and atmospheric deposition or surface runoff, respectively. (4) The average comprehensive noncarcinogenic health risk indices for adults and children were 4.2259E-02 and 1.4194E-01, respectively, which were within the slight risk range, indicating that the risk caused by SHMs to the human body can be almost negligible. This study adopted a mixed method to reveal the risk of SHMs pollution and its sources, which provides some reference and technical support for traceability analysis, zoning control, and health risk studies of regional pollutants and is helpful for formulating scientific management measures and targeting control policies.

Visualization analysis of literature on sudden poisoning incidents in China from 2013 to 2022 based on CiteSpace and VOSviewer

Objective: To use CiteSpace and VOSviewer software to perform visualization analysis on research literature on sudden poisoning incidents in China from 2013 to 2022, in order to grasp the research hotspots and priorities, current research development status and trend of sudden poisoning incidents, and provide reference and ideas for subsequent research. Methods: In October 2023, literature related to sudden poisoning incidents was searched using the China National Knowledge Infrastructure and Wanfang databases. The bibliometric analysis software CiteSpace 6.1.R2 and VOSviewer 1.6.19 were used to visually analyze factors such as authors, institutions, journals, regions, and keywords in the relevant literature. Results: A total of 2578 valid literatures were included, and the overall number of published literatures showed a discontinuous downward trend in recent 10 years. The relevant literature was distributed in 310 journals. Among the top 10 institutions with the highest number of publications, 8 were centers for disease control and prevention at all levels, and the institution with the highest number of publications was the Chinese Center for Disease Control and Prevention (208 articles), but the cooperation between institutions was not obvious. The 2578 articles involved a total of 6549 authors (a total of 8572 appearances, with an average of 1.31 times per person), with a collaboration rate of 2.54 (6549/2578) and 2153 co-authored articles (with 2 or more authors in the literature), with a co-authorship rate of 83.51%. 643 articles (24.94%) received funding support. The region with the highest number of publications was Guangdong Province, with a total of 380 articles (14.74%). Keyword highlighting analysis showed that the research heat of pesticide poisoning, toxic mushrooms, disease characteristics, outbreak events, traceability analysis, outbreaks, and occupational diseases continued to 2022. Conclusion: There are many literatures on sudden poisoning incidents in China, so it is necessary to strengthen the cooperation and communication between institutions and authors. In practical work, the ability of prevention, control, and detection of poisons should be continuously improved to reduce the incidence and degree of harm caused by sudden poisoning incidents.

The Characteristics and Traceability Analysis of the Overflow Pollution During the Flood Season in an Urban Area

The issue of combined sewer overflow (CSO) triggered by rainfall has become a significant obstacle to the improvement of water environment quality. This study conducted a long-term monitoring of three types of rainwater outlets, i.e., combined sewer overflows (Test-CSO), separated sewer outlets (Test-SSO), and partially separated sewer outlets (Test-PSSO), to reveal the characteristics of overflow pollution and trace its sources by monitoring the pollutants from different underlying surfaces across various urban functional areas. The results showed that the major pollutants in overflow events exhibited the following order: COD ≥ TSS > TN > TAN > TP. Rainwater elevated COD and TSS in the Test-CSO, while reducing nitrogen and phosphorus concentrations by dilution. The Test-PSSO experienced varying degrees of overflow pollution, primarily due to the sewer sediment. A negative relationship between the rainfall and peak time of overflow pollution was observed. The traceability analysis indicated the overall pollution intensity exhibited the following order: residential areas > industrial parks > commercial areas. In addition to commercial areas, the pollution intensity across underlying surfaces generally exhibited the following order: roofs > roads > grasslands. The roof runoff was an important source of pollutants for overflow pollution, and TSS and COD were the major contributors. Notably, grasslands had a buffering effect on pollutants and pH.

Multi-omics and bioinformatics for the investigation of therapeutic mechanism of roucongrong pill against postmenopausal osteoporosis

Ethnopharmacological relevanceThe Roucongrong Pill (RCRP), originating from the historical General Medical Collection of Royal Benevolence, is frequently used to treat postmenopausal osteoporosis (PMOP). Despite its prevalent application, the specific anti-osteoporotic mechanisms of RCRP remain to be elucidated. Aim of the studyThis study aims to elucidate the therapeutic mechanism of RCRP in the context of ovariectomy (OVX)-induced PMOP in rats. By employing an integrative approach, the research combines medicinal chemistry, gut microbiota (GM) profiling, metabolomics, MetOrigin traceability, network pharmacology, molecular docking, and molecular dynamics simulations to deliver a comprehensive analysis. Materials and methodsSprague-Dawley (SD) rats underwent bilateral OVX to establish a PMOP model. The therapeutic efficacy of RCRP was evaluated through bone metrics (BMD, bone strength, BV/TV, Tb.Sp), hematoxylin and eosin (H&E) histological assessment, and bone metabolism markers (OPG, BALP, TRACP-5b, β-CTX, RANKL). Fecal metabolomics and 16S rDNA sequencing were employed to assess the influence of RCRP on GM and metabolite profiles. Furthermore, MetOrigin facilitated the traceability analysis of relevant metabolites. Molecular docking identified potential RCRP compounds with anti-PMOP activity, while their stability and protein interactions were assessed through molecular dynamics simulations. Network pharmacology further confirms the targets of action. ResultsRCRP alleviated PMOP in rats, enhancing bone strength, cortical and trabecular BMD, BV/TV, and serum OPG levels, while reducing Tb.Sp, serum BALP, TRACP-5b, β-CTX, and RANKL concentrations. A total of twenty-six distinct metabolites were identified, of which ten—tribufos, sulfoacetic acid, betamethasone dipropionate, 9-oxooctadeca-10,12,15-trienoic acid, menatetrenone, piperlongumine, maltopentaose, enol-phenylpyruvate, catechol, pentaacetate, and (+)-2-methylpropanoic acid—exhibited correlations with six GM species: Turicibacter, Roseburia, Colidextribacter, Helicobacter, Odoribacter, and Lachnoclostridium, as determined by Spearman's correlation analysis. Notably, MetOrigin revealed the microbial metabolism of taurine and hypotaurine, along with host-specific steroid hormone synthesis. Computational docking studies demonstrated robust interactions between five RCRP-derived steroids (hydroxyecdysone, corticosterone, trilostane, 5α-androstan-3,6,17-trione, and cortisol) and key enzymes (estradiol 17α-dehydrogenase and UDP-glucuronosyltransferase), suggesting a potential enhancement of therapeutic efficacy against PMOP. Furthermore, molecular dynamics simulations indicated stable interactions between hydroxyecdysone and two proteins, with binding free energies of −67.427 kJ/mol and −156.948 kJ/mol, respectively. Through network pharmacology and molecular docking approaches, potential targets of these metabolites were identified, including estrogen receptors ESR1 and ESR2, dual specificity phosphatase 6 (DUSP6), sex hormone-binding globulin (SHBG), prostaglandin E receptor 4 (PTGER4), cannabinoid receptor 2 (CNR2), cathepsin K (CTSK), and androgen receptor (AR). ConclusionsRCRP effectively mitigates OVX-induced bone loss in PMOP rats by modulating GM and associated metabolites, along with their potential targets and key metabolic pathways, including taurine and hypotaurine metabolism, as well as steroid hormone biosynthesis. These findings offer new insights into the therapeutic mechanisms by which RCRP may alleviate PMOP.

A new method for the rapid identification of external water types in rainwater pipeline networks using UV–Vis absorption spectroscopy

Ultraviolet–visible (UV–Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV–Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.

Source and succession of microbial communities and tetramethylpyrazine during the brewing process of compound-flavor Baijiu.

Pyrazines are important flavor components and healthy active components in Baijiu, which including tetramethylpyrazine (TTMP). During the brewing process, the traceability of microbial communities and the content distribution characteristics of TTMP are important for improving the quality and style characteristics of compound-flavored Baijiu (CFB). However, the traceability analysis of microorganisms in fermented grains (FG)-used in the production of CFB-lacks quantitative and systematic evaluation. In this study, the microbial communities and TTMP content of Jiuqu (JQ), Liangpei (LP), FG, and pit mud (CP) used in CFB production were characterized; further, coordinate and discriminant analyses were employed to determine differences in microbial communities. Additionally, traceability and correlation analyses were performed to reveal the origin of microbial communities in FG. The source, content, and distribution characteristics of TTMP based on the brewing process have also been discussed. The results showed that most of the bacterial and fungal communities at different levels of FG came from other sources, and the microorganisms of Cladosporium, Acetobacter, Aspergillus, Methanosarcina, and Bacillus were considered have a osculating correlations with TTMP content of FG. Taken together, this study provides insights into the origin of microbial communities in FG and the distribution characteristics of TTMP based on the CFB brewing process. The current findings are conducive for optimizing the fermentation process and improving the quality and style characteristics of CFB.

Decoding the Preparation Stage of Target Shooting under Audiovisual Restricted Conditions: Investigating Neural Mechanisms Using Microstate Analysis.

Shooting is a fine sport that is greatly influenced by mental state, and the neural activity of brain in the preparation stage of shooting has a direct influence on the level of shooting. In order to explore the brain neural mechanism in the preparation stage of pistol shooting under audiovisual restricted conditions, and to reveal the intrinsic relationship between brain activity and shooting behavior indicators, the electroencephalography (EEG) signals and seven shooting behaviors including shooting performance, gun holding stability, and firing stability, were experimentally captured from 30 shooters, these shooters performed pistol shooting under three conditions, normal, dim, and noisy. Using EEG microstates combined with standardized low-resolution brain electromagnetic tomography (sLORETA) traceability analysis method, we investigated the difference between the microstates characteristics under audiovisual restricted conditions and normal condition, the relationship between the microstates characteristics and the behavioral indicators during the shooting preparation stage under different conditions. The experimental results showed that microstate 1 corresponded to microstate A, microstate 2 corresponded to microstate B, and microstate 4 corresponded to microstate D; Microstate 3 was a unique template, which was localized in the occipital lobe, its function was to generate the "vision for action"; The dim condition significantly reduced the shooter's performance, whereas the noisy condition had less effect on the shooter's performance; In audiovisual restricted conditions, the microstate characteristics were significantly different from those in the normal condition. Microstate 4' parameters decreased significantly while microstate 3' parameters increased significantly under restricted visual and auditory conditions; Dim condition required more shooting skills from the shooter; There was a significant relationship between characteristics of microstates and indicators of shooting behavior; It was concluded that in order to obtain good shooting performance, shooters should improve attention and concentrate on the adjustment of collimator and target's center leveling relation, but the focus was slightly different in the three conditions; Microstates that are more important for accomplishing the task have less variation in their characteristics over time; Similar conclusions to previous studies were obtained at the same time, i.e., increased visual attention prior to shooting is detrimental to shooting performance, and there is a high positive correlation with microstate D for task completion. The experimental results further reveal the brain neural mechanism in the shooting preparation stage, and the extracted neural markers can be used as effective functional indicators for monitoring the brain state in the shooting preparation stage of pistols.

Genomic approaches for almond traceability from nursery and along the food chain

Almond is widely cultivated in the world thanks to the quality and healthy features of the kernel. Almond kernel is consumed fresh or employed in the food industry. Hundreds of almond cultivars were selected throughout the long history of cultivation; in this context, an efficient method for varietal identification is essential to ensure cultivar traceability along the chain. This study surveyed the widely employed commercial kits and protocols for DNA extraction from several almond matrices including leaves, kernels (fresh and roasted) and several processed products. Commercial kits (though with minor modification) outperformed the other extraction methods for the isolation of DNA suitable for molecular analysis from all the tested matrices. In parallel, a germplasm collection composed of 140 accessions (123 Sicilian genotypes complemented with widely known national and international cultivars) was genotyped with the Axiom™ 60K almond SNP Array enabling the detection of 6 374 unique SNPs that can be readily used for varietal traceability. A subset of unique SNPs was further validated employing a high-resolution melting (HRM) assay on a discovery panel encompassing ten of the most widely cultivated accessions. The DNA extracted from leaves and kernels of five cultivars was genotyped with eight SSRs allowing the identification of the maternal origin of each kernel. The paper integrates the survey of the widely employed protocols for DNA extraction with the high-throughput genotyping of 140 almond accessions. In this context, unique SNPs validated and optimized for an HRM assay and the availability of SSR markers demonstrated their efficacy in traceability analysis along the chain.

Trustworthy Fault Diagnosis Method Based on Belief Rule Base With Multisource Uncertain Information for Vehicle

This paper develops a new trustworthy fault diagnosis (TFD) method based on belief rule base expert system with combining multi-source uncertainty information for vehicle, such as flying machine. To improve the performance of fault diagnosis for vehicle, a new belief rule base expert system with multi-source uncertainty information (BRB-MU) is developed, where the influence of the random environment disturbance is addressed by introducing uncertain factors of input characteristics. In BRB-MU, the uncertain factors of input characteristics are calculated by the signal to noise ratio based method. The new developed BRB-MU model aims to handle three problems: unbalanced observation data of system states, uncertain expert knowledge and random environment disturbance. The first two problems are solved by the combination of expert knowledge and observation data. In parallel, the third problem is handled by the new introduced uncertain factors based on singular value decomposition (SVD) in BRB-MU. To quantitatively analyze the influence of the uncertain information to the TFD output, the traceability analysis of BRB-MU is conduced that can provide support for decision making of vehicle optimization design. It is analyzed based on the modeling traceability and parameters interpretability. To demonstrate the effectiveness of the TFD method, an experiment illustration is conduced.

Deep Smooth Random Sampling and Association Attention for Air Quality Anomaly Detection

Real-time monitoring and timely warning of air quality are vital components of building livable cities and implementing the “Healthy China” strategy. Real-time, efficient, and accurate detection of air quality anomalies holds great significance. However, almost all existing methods for air quality anomaly detection often overlook the imbalanced distribution of data. In addition, many traditional methods cannot learn both pointwise representation and pairwise association, so they cannot solve complex features. This study proposes an anomaly detection method for air quality monitoring based on Deep Smooth Random Sampling and Association Attention in Transformer (DSRS-AAT). Firstly, based on the third geographical law, the more similar the geographical environment, the closer the geographical target features are. We cluster sites according to the surrounding geographic features to fully explore latent feature associations. Then, we employ Deep Smooth Random Sampling to rebalance the air quality datasets. Meanwhile, the Transformer with association attention considers both prior associations and series associations to distinguish anomaly patterns. Experiments are carried out with real data from 95 monitoring stations in Haikou City, China. Final results demonstrate that the proposed DSRS-AAT improves the effectiveness of anomaly detection and provides interpretability analysis for traceability, owing to a significant improvement with the baselines (OmniAnomaly, THOC, etc.). The proposed method effectively enhances the effectiveness of air quality anomaly detection and provides a reference value for real-time monitoring and early warning of urban air quality.

Throat microbiota drives alterations in pulmonary alveolar microbiota in patients with septic ARDS

ABSTRACT Objectives The translocation of intestinal flora has been linked to the colonization of diverse and heavy lower respiratory flora in patients with septic ARDS, and is considered a critical prognostic factor for patients. Methods On the first and third days of ICU admission, BALF, throat swab, and anal swab were collected, resulting in a total of 288 samples. These samples were analyzed using 16S rRNA analysis and the traceability analysis of new generation technology. Results On the first day, among the top five microbiota species in abundance, four species were found to be identical in BALF and throat samples. Similarly, on the third day, three microbiota species were found to be identical in abundance in both BALF and throat samples. On the first day, 85.16% of microorganisms originated from the throat, 5.79% from the intestines, and 9.05% were unknown. On the third day, 83.52% of microorganisms came from the throat, 4.67% from the intestines, and 11.81% were unknown. Additionally, when regrouping the 46 patients, the results revealed a significant predominance of throat microorganisms in BALF on both the first and third day. Furthermore, as the disease progressed, the proportion of intestinal flora in BALF increased in patients with enterogenic ARDS. Conclusions In patients with septic ARDS, the main source of lung microbiota is primarily from the throat. Furthermore, the dynamic trend of the microbiota on the first and third day is essentially consistent.It is important to note that the origin of the intestinal flora does not exclude the possibility of its origin from the throat.

Distribution and survival of pathogens from different waste components and bioaerosol traceability analysis in household garbage room

Household garbage rooms release abundant bioaerosols and are an important source of pathogens; however, information on the distribution and survival patterns of pathogens in different waste components is limited. In this study, a culture method and 16S rRNA high-throughput sequencing were used to determine bacterial communities, culturable pathogens, and human bacterial pathogens (HBPs). The results showed that abundant culturable bacteria were detected in all waste types, and a large number of S. aureus was detected on the surface of recyclable wastes, whereas S. aureus, total coliforms, Salmonella, Enterococcus, and hemolytic bacteria were detected in food waste and other waste. The activities of these detected pathogenic bacteria decreased after 24 h of storage but re-activated within one week. Factors affecting the emergence of pathogens varied with different waste components. Sequencing results showed that Pseudomonas, Acinetobacter, and Burkholderia were abundant in the waste samples, whereas Achromobacter, Exiguobacteriums, Bordetella, and Corynebacterium were the primary pathogens in the bioaerosol and wall attachment. The results of traceability analysis showed that bioaerosol microbes were mainly derived from raw kitchen waste (5.98%) and plastic and paper contaminated with food waste (19.93%) in garbage rooms. In addition, bioaerosols were the main source of microflora in the wall attachment, which possessed high HBP diversity and required more attention. These findings will help in understanding the microbial hazards in different waste components and provide guidance for the control and risk reduction of bioaerosols during waste management and recycling.

Germplasm evaluation and identification of the population source of hard clam (Mercenaria mercenaria) in China using SSR and COI markers

In aquaculture, good germplasm resources are nearly decisive for its development. The hard clam (Mercenaria mercenaria), a significant marine aquaculture bivalve in China, has flourished quickly since being introduced from North America in 1997. However, for numerous reasons, it has suffered from germplasm deterioration and uncertain origins. To address these issues, seven microsatellite DNA loci (SSRs) and mtCOI were employed to evaluate germplasm and trace the origins of various Chinese populations. The results of these two molecular genetic markers revealed weak genetic differentiation among all Chinese populations, with high genetic diversity observed across most individuals, except for a few outliers. This suggests an alarming situation regarding germplasm resources in China. Based on results of traceability analysis, the most likely areas of origin for the Chinese populations are Chesapeake Bay and the southern region of Cape Hatteras. This study provides a crucial theoretical foundation for the cultivation, conservation, and subsequent introduction of hard clam populations in China, thereby ensuring the sustainable growth of the hard clam breeding sector.

Efficacy assessment for multi-vehicle formations based on data augmentation considering reliability

Nowadays, aerial vehicle swarm (AVS) formations have been widely applied to military actions. Meanwhile, assessing their efficacy has also received increasing attention due to the significance for offline tactic planning and online formation switching, which places extra emphasis on the combination of empirical knowledge and experimental data, the transparency of assessment process and the explainability of assessment results. In practical engineering, the efficacy data collected from AVS formations are often incomplete due to limited experiments, and unreliable due to multi-source uncertainties, which poses a great challenge to the development of efficacy assessment system that balances reliability, explainability and accuracy. Oriented to the efficacy assessment of AVS formations under incomplete and unreliable efficacy data, the solution based on belief rule base (BRB) is developed. The reliable heterogeneous data augmentation (RHDA) method is first proposed to improve the availability of efficacy data collected from AVS formation with destination configuration by those collected from AVS formation with source configuration. Then, the feature reliability calculation method based on configuration similarity is designed to avoid the interference of subjective intention. Besides, the configuration importance measurement method based on first-order traceability analysis is put forward to provide decision-makers with references for selecting the most important source configuration when multiple ones exist. The feasibility and effectiveness of developed solution are verified by appropriate simulation experiments.

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

The origin of seeds is a crucial environmental factor that significantly impacts crop production. Accurate identification of seed origin holds immense importance for ensuring traceability in the seed industry. Currently, traditional methods used for identifying the origin of maize seeds involve mineral element analysis and isotope fingerprinting, which are laborious, destructive, time-consuming, and suffer from various limitations. In this experiment, near-infrared spectroscopy was employed to collect 1360 maize seeds belonging to 12 different varieties from 8 distinct origins. Spectral information within the range of 11,550–3950 cm−1 was analyzed while eliminating multiple interferences through first-order derivative combined with standard normal transform (SNV). The processed one-dimensional spectral data were then transformed into three-dimensional spectral maps using Gram’s Angle Field (GAF) to be used as input values along with the VGG-19 network model. Additionally, a convolution layer with a step size of 1 × 1 and the padding value set at 1 was added, while pooling layers had a step size of 2 × 2. A batch size of 48 and learning rate set at 10−8 were utilized while incorporating the Dropout mechanism to prevent model overfitting. This resulted in the construction of the GAF-VGG network model which successfully decoded the output into accurate place-of-origin labels for maize seed detection. The findings suggest that the GAF-VGG network model exhibits significantly superior performance compared to both the original data and the PCA-based origin identification model in terms of accuracy, recall, specificity, and precision (96.81%, 97.23%, 95.35%, and 95.12%, respectively). The GAF-VGGNet model effectively captures the NIR features of different origins of maize seeds without requiring feature wavelength extraction, thereby reducing training time and enhancing accuracy in identifying maize seed origin. Moreover, it simplifies near-infrared (NIR) spectral modeling complexity and presents a novel approach to maize seed origin identification and traceability analysis.

Traceability Analysis and Environmental Quality Assessment of Soil Heavy Metal Pollution in West Hunan Province

In order to explore the status of soil heavy metal pollution and environmental quality in west Hunan, relevant areas of Phoenix County were selected as the study area. Using data from 440 soil samples collected in the study area from June to August 2022, the pH value of the soil and contents of eight heavy metal elements, namely, As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, were analyzed. The PMF model was used for traceability analysis and geochemical evaluation of soil environmental quality. The results showed that the average values of soil heavy metals ω(Zn), ω(Cr), ω(Pb), ω(Ni), ω(Cu), ω(As), ω(Cd), and ω(Hg) were 81.02, 64.67, 31.63, 29.27, 25.52, 9.93, 0.28, and 0.13 mg·kg-1, respectively. The soil in the study area was mainly weakly acidic, and the contents of the Cd and Hg elements were relatively high compared to the national soil background values and were highly variable. The contents of the Hg and Cd elements in forest land were higher than that in other land uses. The PMF model results showed that the contribution rates of heavy metal pollution sources in the study area were mining sources (37.4%), atmospheric sedimentation sources (7.7%), natural sources (41.1%), and agricultural activity sources (13.8%) and provided suggestions on pollution control measures according to the spatial distribution of the four types of pollution sources. Through the comprehensive assessment of soil environmental geochemistry, the study area was divided into three types of plots, namely, non-risk areas (94.27 km2), accounting for 76.38%; risk-controllable areas (27.45 km2), accounting for 22.24%; and high-risk areas (1.7 km2), accounting for 1.38%. This study provided data support for the prevention and control measures of land pollution in the research area, as well as the delineation of the prevention and control scope.

Spatiotemporal distribution, interactions and toxic effect of microorganisms and ARGs/MGEs from the bioreaction tank in hospital sewage treatment facility

Antibiotic resistance genes (ARGs) can be emitted from wastewater to ambient air and impose unignorable inhalable hazards, which could be exacerbated in antibiotic-concentrated hospital sewage. However, whether the ARG-carrying pathogens are more likely to infect cells remains largely unknown. Here, this study investigated and analyzed the spatiotemporal distribution, interaction, and toxicity of airborne microorganisms and their hosting ARGs in a hospital sewage treatment facility. The average concentration of ARGs/MGEs in sewage of bioreaction tank (BRT-W) was 2.27 × 104 gene copies/L. In the air of bioreaction tank (BRT-A), the average concentration of ARGs/MGEs was 15.86 gene copies/m3. In the four seasons, the ARGs concentration of sewage gradually decreased over time; The concentration of ARGs in the air first decreased and then increased. In spring, the concentration of ARGs/MGEs (qacedelta1-01) in BRT-W was highest (1.05 × 105 gene copies/L); The concentration of ARGs/MGEs (strB) in BRT-A in winter was higher than other seasons (26.18 gene copies/m3). Different from the past, this study also paid attention to the pathogenic potential of ARGs/MGEs in the air. The results of cell experiments showed that the cytotoxicity of drug-resistant Escherichia coli could reach Grade V. This suggested that the longer the drug-resistant E. coli were exposed to cells, the greater the cytotoxicity. Moreover, the cytotoxicity of bacteria increased with the increase in exposure time. In spring, the toxic effect of ARGs/MGEs in sewage of BRT-W was highest. Traceability analysis proved that BRT-W was an essential source of microorganisms and ARGs/MGEs in BRT-A. Furthermore, the combined risk of people exposed to the air of BRT in spring was higher than that in other seasons.