Search Results (160)

Global warming and anthropogenic climate change are projected to expand the geographic distribution and population abundance of ectothermic species and exacerbate the biological invasion of exotic species. DNA methylation, as a reversible epigenetic modification, could provide a putative link between the phenotypic plasticity of invasive species and environmental temperature variations. We assessed and interpreted the epigenetic mechanisms of invasive and indigenous species’ differential tolerance to thermal stress through the invasive species Bemisia tabaci Mediterranean (MED) and the indigenous species Bemisia tabaci AsiaII3. We examine their thermal tolerance following exposure to heat and cold stress. We found that MED exhibits higher thermal resistance than AsiaII3 under heat stress. The fluorescence-labeled methylation-sensitive amplified polymorphism (F-MSAP) results proved that the increased thermal tolerance in MED is closely related to DNA methylation changes, other than genetic variation. Furthermore, the quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting analysis of DNA methyltransferases (Dnmts) suggested that increased expression of Dnmt3 regulates the higher thermal tolerance of female MED adults. A mechanism is revealed whereby DNA methylation enhances thermal tolerance in invasive species. Our results show that the Dnmt-mediated regulation mechanism is particularly significant for understanding invasive species’ successful invasion and rapid adaptation under global warming, providing new potential targets for controlling invasive species worldwide. Full article

Botulism is a severe muscle paralysis disease mediated by the botulinum toxin. Here, we reported a foodborne botulism case caused by Clostridium botulinum subtype A5(b3) from self-packaged vacuum spicy rabbit heads. Treatment for this case was delayed due to misdiagnosis and insufficient diagnostic capacity in three hospitals, which resulted in progressive clinical deterioration, and eventually, the patient was transferred to Shandong Public Health Clinical Center for specialized therapy. The case was suspected as foodborne botulism by the Qilu Medical-Prevention Innovation Integration pathway and multi-disciplinary consultation. An epidemiological investigation and laboratory confirmation revealed that the botulinum neurotoxin originated from vacuum-packaged spicy rabbit heads distributed via interprovincial cold chain logistics. After treatment with botulism antiserum, the patient’s condition significantly improved, and they were discharged after recovery. We revealed that this foodborne botulism outbreak was caused by the Clostridium botulinum A5(b3) subtype from food by whole-genome sequencing and SNP typing. All the strains belonged to Group I carrying the botulinum neurotoxin gene classified as the ha cluster. Toxin A was confirmed by MBA and other methods, while toxin B was non-functional due to the truncated bont/B gene. Other virulence genes and antibiotic resistance genes were also detected. Our findings indicate that self-packaged vacuum meat products represent an emerging risk factor for botulism transmission when stored improperly. Importantly, the recurrent misdiagnosis in this case underscored the urgent need to enhance the training of healthcare professionals in medical institutions to improve the diagnostic accuracy and clinical management of botulism. Full article

In this paper, a discrete element method (DEM) coupled with a finite element method (FEM) was used to elucidate the impact of packing structures and size ratios on the cold die compaction behavior of pure copper powders. HCP structure, SC structure, and three random packing structures with different particle size ratios (1:2, 1:3, and 1:4) were generated by the DEM, and then simulated by the FEM to analyze the average relative density, von Mises stress, and force chain structures of the compact. The results show that for HCP and SC structures with a regular stacking structure, the average relative densities of the compact were higher than those of random packing structures, which were 0.9823, 0.9693, 0.9456, 0.9502, and 0.9507, respectively. Compared with their initial packing density, it could be improved by up to 21.13%. For the bigger particle in HCP and SC structures, the stress concentration was located between the adjacent layers, while in the small particles, it was located between contacted particles. During the initial compaction phase, smaller particles tend to occupy the voids between larger particles. As the pressure increases, larger particles deform plastically in a notable way to create a stabilizing force chain. This action reduces the axial stress gradient and improves radial symmetry. The transition from a contact-dominated to a body-stress-dominated state is further demonstrated by stress distribution maps and contact force vector analysis, highlighting the interaction between particle rearrangement and plasticity. Full article

China’s National Famous, Special, Excellent, and New Agricultural Products are key rural economic assets, yet their spatial patterns and sustainability drivers remain underexplored. Based on the geospatial data of 1932 National Famous, Special, Excellent and New Agricultural Products in China, this study systematically analyzes their spatial distribution pattern by using GIS spatial analysis techniques, including the standard deviation ellipse, kernel density estimation, geographic concentration index and Lorenz curve, and quantitatively explores the driving factors of sustainable development by using geographic detectors. The research results of this paper are as follows. (1) The spatial distribution shows a significant non-equilibrium characteristic of “high-density concentration in the central and eastern part of the country and low-density sparseness in the western part of the country” and the geographic concentration index (G = 22.95) and the standard deviation ellipse indicate that the center of gravity of the distribution is located in the North China Plain (115° E–35° N), and the main direction extends along the longitude of 110° E–120° E. (2) Driving factor analysis showed that railroad mileage (X10) (q = 0.5028, p = 0.0025 < 0.01), highway mileage (X11) (q = 0.4633, p = 0.0158 < 0.05), and population size (X3) (q = 0.4469, p = 0.0202 < 0.05) are the core drivers. (3) Three-dimensional kernel density mapping reveals that the eastern coast and central plains (kernel density > 0.08) form high-density clusters due to the advantages of the transportation network and market, while the western part shows a gradient decline due to the limitation of topography and transportation conditions. The study suggests that the sustainable development of National Famous, Special, Excellent, and New Agricultural Products should be promoted by strengthening transportation and digital logistics systems, enhancing cold-chain distribution for perishable goods, tailoring regional branding strategies, and improving synergy among local governments, thereby providing actionable guidance for policymakers and producers to increase market competitiveness and income stability. The study provides a quantitative, policy-oriented assessment of China’s branded agricultural resource allocation and its sustainability drivers, offering specific recommendations to guide infrastructure investment, e-commerce logistics enhancement, and targeted subsidy design for balanced regional development. The study highlights three key contributions: (1) an innovative integration of geospatial analytics and geographical detectors to reveal spatial patterns; (2) clear empirical evidence for policymakers to prioritize transport and digital logistics investments; and (3) practical guidance for producers and brand managers to enhance product market reach, optimize supply chains, and strengthen regional competitiveness in line with sustainable development goals. Full article

In the past two decades, particularly through the pandemic, the demand for real-time logistics has significantly increased. Cold chain logistics ensures specific temperature conditions for perishable goods such as food and pharmaceuticals, which is crucial for maintaining product quality, safety, and regulatory compliance. The integration of the Internet of Things (IoT) into cold chain logistics has transformed supply chain operations. The COVID-19 pandemic and the global urgency for vaccine distribution accelerated the adoption of cold chain technologies, emphasizing their role in preserving perishable goods’ integrity. IoT enables real-time monitoring, remote control, predictive analytics, and data-driven decision-making, all of which are essential for modern logistics. We conducted a bibliometric analysis of 50 publications from 1997 to 2024 to examine IoT’s role in cold chain management. Through co-occurrence and co-citation network analysis, core themes, influential works, and major contributors were identified. Thematic mapping highlighted the importance of temperature monitoring, logistics optimization, and risk management. Additionally, the transition from conventional logistics practices to IoT-driven methodologies was investigated in cold chain operations. The findings of this study provide a basis for understanding the structural properties of co-citation and co-occurrence networks in cold chain logistics and the evolving landscape of cold chain technology, and its impact on logistics, emphasizing the importance of intelligent, reliable, and sustainable cold chain systems to meet the growing demands in global supply chains. Full article

Background: Poliomyelitis is a vaccine-preventable disease, with oral poliomyelitis vaccines (OPVs) and injectable poliomyelitis vaccines. In the Democratic Republic of the Congo (DRC), circulating vaccine-derived polioviruses (VDPVs) persist due to intrinsic and extrinsic factors, including the quality of the cold chain, which may make the vaccines less effective. This study’s objective was to evaluate the cold chain’s quality of OPVs and its effect on the vaccine’s viability and potency at different levels in health systems in Kinshasa. Methods: A cross-sectional study was conducted in Kinshasa, collecting OPVs at different levels of the health pyramid. Vaccine viability was assessed by cell culture using a modified World Health Organization (WHO) protocol, and the viral titer was determined using the Karber formula. The vaccine titer was classified as “very good”, “good”, or “poor” according to the WHO standard’s viral titer. Results: A total of 53 vaccines were collected and analyzed, compressing 38 bivalent oral poliomyelitis (bOPV) vaccines and 15 novel oral poliomyelitis vaccines, type 2 (nOPV2). The viral titer ranged from log10^5.8 to log 10^7.3 and from log10^5.4 to log10^8.9 for the nOPV2 and the bOPV, respectively. Of these 53 vaccine samples, 10% of the bOPVs showed viral titers below the recommended WHO threshold (>10⁶ CCID₅₀/dose), 100% of the nOPV2 had viral titers within the WHO standards (>10⁵ CCID₅₀/dose), and a significant decline in the viral titer was observed for both types of vaccines (nOPV2 and bOPV) as the distribution progressed along the level of the health pyramid. Conclusions: This study demonstrated that the viral titer of OPV declined from central to peripheral areas in routine and campaign strategies in Kinshasa. Full article

Cold chain logistics is crucial in ensuring food quality and safety in modern supply chains. The required temperature control systems increase operational costs and environmental impacts compared to conventional logistics. To reduce these costs while maintaining service quality in real-world distribution scenarios, efficient route planning is essential, particularly when products with different temperature requirements need to be delivered together using multi-compartment refrigerated vehicles. This substantially increases the complexity of the routing process. We propose a novel deep reinforcement learning approach that incorporates a vehicle state encoder for capturing fleet characteristics and a dynamic vehicle state update mechanism for enabling real-time vehicle state updates during route planning. Extensive experiments on a real-world road network show that our proposed method significantly outperforms four representative methods. Compared to a recent ant colony optimization algorithm, it achieves up to a 6.32% reduction in costs while being up to 1637 times faster in computation. Full article

In this work, we aim to adjust vehicle speeds in real time by predicting the surrounding population density based on the spacing of customer locations. We comprehensively consider fixed costs, cargo loss costs, fuel costs, penalty costs, and environmental costs; build a cold chain distribution vehicle path optimization model with the goal of minimizing the total cost and maximizing customer satisfaction; and design a hybrid genetic algorithm solution optimization model. The algorithm dynamically adjusts the tournament scale through the standard deviation of the fitness value, uses the OX cross operator, determines the position of variation based on the customer information matrix, and performs local search optimization with the removal and insertion operators. Through comparison to other algorithms in the literature, the results show that the hybrid genetic algorithm not only improves customer satisfaction, but also maintains a lower total cost, which is obviously superior when solving the complex cold chain distribution path optimization problem; further comparison and analysis of the mathematical model in this paper with the single-dimension satisfaction model reveals that under the same satisfaction constraint threshold, the model in this paper can significantly reduce the system operating cost; we also deeply discuss the influence mechanism of vehicle traveling mode and customer point sparsity radius on distribution path planning. Full article

The objective of this study is to explore the cold chain system in a metropolitan area, focusing on the overall system cost encompassing both distribution centers and transportation. The research delves into the planning of urban cold chain systems, considering fluctuating minimum customer demands, the traffic conditions of potential new centers, and the variability in carbon-trading prices. To manage the complexity of these objectives and inherent uncertainties, we introduce a flexible chance-constrained programming model for the cold chain system (FCCP-CCS). An FCCP-CCS programming model is developed to address the multifaceted goals and various uncertainties. The effectiveness of this model is validated through experimental analysis using real-world data from a major city’s cold chain system. The findings of this study reveal several key insights: (1) The levels of confidence and satisfaction significantly impact system optimization, with higher levels leading to increased consumption. (2) Customer demand variations would determine the transportation and the potential new centers in the system. (3) The surroundings of a distribution center partly indicate its service quality. (4) Governmental adjustments in carbon-trading prices can effectively enhance the overall sustainability of the urban cold chain system. This research highlights the importance of optimization in designing and managing urban cold chain systems, particularly in environmental sustainability. Full article

The sharing economy is an inevitable trend in cold chain logistics. Most cold chain logistics enterprises are small and operate independently, with limited collaboration. Joint distribution is key to integrating cold chain logistics and the sharing economy. It aims to share logistics resources, provide collective customer service, and optimize distribution routes. However, existing studies have overlooked uncertainty factors in joint distribution optimization. To address this, we propose the Cold Chain Logistics Joint Distribution Vehicle Routing Problem with Time-Varying Network (CCLJDVRP-TVN). This model integrates traffic congestion uncertainty and constructs a time-varying network to reflect real-world conditions. The solution combines simulated annealing strategies with genetic algorithms. It also uses the entropy mechanism to optimize uncertainties, improving global search performance. The method was applied to optimize vehicle routing for three cold chain logistics companies in Beijing. The results show a reduction in logistics costs by 18.3%, carbon emissions by 15.8%, and fleet size by 12.5%. It also effectively addresses the impact of congestion and uncertainty on distribution. This study offers valuable theoretical support for optimizing joint distribution and managing uncertainties in cold chain logistics. Full article

If lettuce is contaminated in the field, Shiga toxin-producing E. coli (STEC) O157:H7 can survive through the distribution chain. Prolonged cold storage during transportation may impact pathogen physiology, affecting subsequent stress survival and virulence. Greenhouse-grown Romaine lettuce, inoculated with three STEC O157:H7 strains, was harvested after 24 h and stored at 2 °C for 5 d following 4 h at harvest temperature (9 °C or 17 °C). Culturable, persister, and viable but non-culturable (VBNC) cells were quantified. Virulence was evaluated using Galleria mellonella and acid tolerance at pH 2.5 and tolerance to 20–25 ppm free chlorine were quantified. Colder harvest temperature (9 °C) before cold storage led to greater transformation of STEC O157:H7 into dormant states and decreased virulence in most cases. Increasing length of cold storage led to decreased virulence and acid tolerance of STEC O157:H7 on lettuce, while having no significant effect on chlorine tolerance. These findings highlight that entry of STEC O157:H7 into dormant states during harvest and transportation at cold temperatures leads to decreased stress tolerance and virulence with increasing cold storage. Changes in STEC O157:H7 physiology on lettuce during cold storage can be integrated into risk assessment tools for producers, which can assist in identifying practices that minimize risk of STEC O157:H7 from consumption of lettuce. Full article

Long-chain acyl-CoA synthetase (LACS) is a crucial enzyme involved in cellular lipid metabolism, playing a significant role in plant development and adaptation to environmental stress. However, our understanding of the CaLACS gene family in pepper remains limited. In this study, we identified nine members of the CaLACS gene in the ‘UCD-10X-F1’ pepper genome and named them CaLACS1-CaLACS9 based on their chromosomal distribution. Phylogenetic analysis revealed that the subfamily I-A includes CaLACS1, CaLACS3, and CaLACS7; the subfamily I-C contains CaLACS2; the subfamily II comprises CaLACS4 and CaLACS8; and the subfamily III consists of the remaining members. Collinearity analysis showed that there were twelve collinear pairs between six CaLACS genes and five AtLACS genes, and two fragment replication gene pairs in the nine CaLACS genes of pepper. Furthermore, numerous cis-acting elements associated with stress response, hormonal regulation, development, and light response were identified in the promoter regions of the CaLACS genes. RNA-seq analysis indicated that CaLACS genes exhibit tissue specificity and are widely expressed in pepper leaves following treatment with exogenous plant hormones, and under conditions of cold, heat, drought, and salt stress. Additionally, virus-induced gene silencing (VIGS) technology was employed to further investigate the roles of CaLACS6 and CaLACS9. Silencing these target genes in pepper seedlings increased their sensitivity to cold stress, as evidenced by the accumulation of reactive oxygen species (ROS), reduced antioxidant defense capacity, and decreased expression levels of cold-responsive and ROS-related genes. The findings of this study provide valuable insights into the functional roles of the CaLACS gene family and highlight CaLACS6 and CaLACS9 as promising candidate genes for enhancing cold tolerance in pepper. Full article

Precise detection of meat freshness levels is essential for food consumer safety and real-time quality monitoring. This study aims to achieve the high-accuracy freshness detection of chilled mutton freshness by integrating hyperspectral imaging with deep learning methods. Although hyperspectral data can effectively capture changes in mutton freshness, sparse raw spectra require optimal data processing strategies to minimize redundancy. Therefore, this study employs a multi-stage data processing approach to enhance the purity of feature spectra. Meanwhile, to address issues such as overlapping feature categories, imbalanced sample distributions, and insufficient intermediate features, we propose a Dual-Branch Hierarchical Spectral Feature-Aware Network (DBHSNet) for chilled mutton freshness detection. First, at the feature interaction stage, the PBCA module addresses the drawback that global and local branches in a conventional dual-branch framework tend to perceive spectral features independently. By enabling effective information exchange and bidirectional flow between the two branches, and injecting positional information into each spectral band, the model’s awareness of sequential spectral bands is enhanced. Second, at the feature fusion stage, the task-driven MSMHA module is introduced to address the dynamics of freshness variation and the accumulation of different metabolites. By leveraging multi-head attention and cross-scale fusion, the model more effectively captures both the overall spectral variation trends and fine-grained feature details. Third, at the classification output stage, dynamic loss weighting is set according to training epochs and relative losses to balance classification performance, effectively mitigating the impact of insufficiently discriminative intermediate features. The results demonstrate that the DBHSNet enables a more precise assessment of mutton freshness, achieving up to 7.59% higher accuracy than conventional methods under the same preprocessing conditions, while maintaining superior weighted metrics. Overall, this study offers a novel approach for mutton freshness detection and provides valuable support for freshness monitoring in cold-chain meat systems. Full article

The Gibberellic Acid Stimulated in Arabidopsis (GASA) gene family is regulated by gibberellins and plays a crucial role in regulating plant growth and development. Based on rubber tree genome data, 18 HbGASA genes, designated HbGASA1 to HbGASA18, were identified in Hevea brasiliensis. Comprehensive bioinformatics analyses were performed to characterize gene structures, chromosomal distributions, syntenic relationships, protein architectures, phylogenetic evolution, and expression profiles. The expression patterns of HbGASA genes under low-temperature stress were further validated by quantitative real-time polymerase chain reactions (qRT-PCR). The results demonstrated that the 18 HbGASA genes were unevenly distributed across 10 chromosomes. The encoded proteins ranged from 88 to 253 amino acids in length, and the number of exons varied from 2 to 4. Phylogenetic analysis clustered these genes into three distinct clades. Conserved motif analysis identified 10 conserved motifs, with Motif 1 and Motif 2 being highly conserved across all members. Promoter analysis revealed multiple hormone-responsive and stress-related regulatory cis-acting elements. Transcripts of the 18 HbGASA genes were detected in various tissues, and significant differences were observed in their expression levels. Under cold stress, qRT-PCR results showed that multiple HbGASA genes were significantly up-regulated. This study provides valuable insights into the structure, evolution, and functional diversification of GASA genes in the important tropical crop, H. brasiliensis. Full article

Saline-alkali stress and cold damage significantly impact the yield of Brassica napus. G proteins play a crucial role in plant resistance to abiotic stresses, and research on G proteins in Brassica napus (rapeseed) is still in its early stages. In this study, we employed bioinformatics tools to systematically investigate the basic physicochemical properties, phylogenetic relationships, distribution, gene structure, cis-regulatory elements, and expansion patterns of the GS3 gene family in Brassica napus. Additionally, reverse transcription polymerase chain reaction (RT-PCR) was used to analyze the response of the BnGS3-3 gene to salt and low-temperature stresses. Natural variations were found in the promoter region of BnGS3-3. By conducting a promoter-driven luciferase (LUC) assay, the relationship between natural variations in the BnGS3-3 promoter and salt and cold tolerance was analyzed. Furthermore, the impact of these natural variations on flowering time, root length, and yield was explored using phenotypic data from a population. Our research results aim to provide insights into the function and molecular mechanisms of BnGS3-3 in Brassica napus, and to offer valuable genetic resources for molecular breeding to improve salt and low-temperature tolerance in Brassica napus. Full article