Production Cycle Research Articles

Effect of innovative peat-free organic growing media and fertilizer on nutrient allocation in pedunculate oak (Quercus robur L.) and European beech (Fagus sylvatica L.) seedlings

This study evaluates the effects of novel peat-free organic growing media and a novel liquid fertilizer on the biometric features and macronutrient allocation of Quercus robur and Fagus sylvatica seedlings with the view to compare biomass and nutrient allocation of plant organs in seedlings cultivated on peat growing medium against those grown on novel peat-free growing medium and fertilizer. The experimental setup involved four growing medium variants, including peat as the control (R20, R21, R22 and C). The novel growing medium and fertilizer were designed and formulated by the University of Agriculture in Kraków, Poland (UAK). Fertilization used in the state forest nurseries was represented as SR20, SR21, and SR22, while the novel fertilizer of UAK was represented as UR20, UR21, and UR22; meanwhile, SC and UC represented the control growing medium (peat) in both cases, respectively. The experiment was laid in a 2 × 2 × 4 experimental design using five seedlings per treatment. Seedlings were assessed for roots, shoots, and leaves biomass. The allocation patterns highlighted the variability of nutrient allocation within the plants, with more nutrients allocated to the root system. Interestingly, treatment UR22 yielded the highest mean root values, root biomass, and virtually all macroelement allocation. The SC solid fertilizer treatment and the UR22 liquid fertilizer treatment consistently showed superior performance across both species and different plant organs. These findings suggest that these treatments are particularly effective in enhancing the nutrient content of oak and beech seedlings, making them suitable choices for optimizing the growth and health of these species. Seedlings were assessed for roots, shoots, and leaves biomass after the nursery production cycle.

Innovative management of the production risks of agricultural enterprises

The article examines the innovative infrastructure in the management of production risks of agricultural enterprises, which is related to the rules for evaluating alternative scenarios for reducing threats in the production system of agricultural production entities in order to obtain the desired result. A methodical approach to the assessment of factors-tools in the innovative management of production risks, which activate the process of economic development of agricultural enterprises in an institutional environment, is presented. It is proved that the institutional determinants that manage the production risks of agricultural enterprises form a system whose effectiveness depends on the implementation of the relevant directions of economic development of agricultural institutes focused on the introduction of innovations into the production cycle. The structural dialectic connection of the concept of innovative development of agricultural enterprises with the cyclical development of the production system is presented. A structural and logical diagram of a methodical approach to the implementation of the mechanism of innovative management of production risks of agricultural enterprises has been built. A mathematical toolkit for evaluating scenarios of innovative management of production risks of agricultural enterprises is defined. Stimulating and disincentive factors-tools in the innovative management of production risks and their impact on the economic development of agricultural enterprises are determined. In order to determine the optimal scenarios for neutralizing threats to the economic development of agricultural enterprises, models of acceleration (deceleration) of the action of stimulating and disincentive factors-tools in the innovative management of production risks were built. The integral index of the economic development of agricultural enterprises of the agro-climatic regions of Ukraine in the pre-war, war-conflict and post-war periods was calculated. It has been established that due to the accumulation of a significant amount of production, financial, material, technical and innovative potential, the level of economic development of agricultural enterprises of the agro-climatic zones of the Forest-Steppe and Polissia increases, which characterizes their ability to reproduce the production system of agriculture in Ukraine.

Advancing sustainability in Electron and laser beam powder Bed Fusion technologies via Innovation: Insights from patent analysis

Additive manufacturing (AM) technologies continuously evolve in materials and operational processes. However, challenges related to energy consumption, material reuse efficiency, and the integration of Circular Economy (CE) principles may impede the overall sustainability of AM processes. As many industrial sectors adopt AM technologies, there is a growing need for innovative tools, methods, and frameworks that guide companies toward more sustainable and circular production practices. This shift involves improving resource efficiency and fostering new Circular Business Models that emphasize material recovery, product lifecycle extension, and waste minimization. This study assesses current and future trends in the development of sustainable AM technologies for processing metal feedstock, based on a comprehensive patent analysis covering the period from 2004 to 2024. We specifically focus on the sustainability impacts of Electron and Laser Beam Powder Bed Fusion (PBF-EB, PBF-LB) and Direct Energy Deposition (DED), given their widespread industrial applications. Patents were categorized into three groups: materials, processes, and components. Furthermore, AM technologies were analyzed according to their role within the production cycle: materials preparation, pre-processing, manufacturing, and post-processing. This categorization allows for a detailed understanding of how innovations in AM contribute to more sustainable production and consumption practices by improving energy efficiency, material usage, circularity, and overall environmental impact.

A novel simplified model to evaluate environmental impact based on life cycle assessment methodology –Case study of spinach considering food waste reduction through modified atmosphere packaging–

Greenhouse gases (GHGs) from food loss and waste (FLW) act as a major hindrance to achieving the Sustainable Development Goal 13. Modified atmosphere packaging (MAP) can reduce the FLW of packaged fruits and vegetables, decreasing FLW-related GHGs. This study assessed the influence of food waste reduction (FWR) via MAP on the environmental load of the spinach life cycle. Particularly, we introduced a novel model to estimate the environmental loads of the life cycle. Additionally, we employed the model for the life cycles of strawberries and peaches to verify the suitability of the model to other product's life cycles. Furthermore, we assessed the trade-offs between the increase in the environmental loads attributed to MAP production and the decrease in the environmental loads of the spinach life cycle via FWR. FWR via MAP decreased the environmental loads of the spinach supply chain by 45.2 % compared to the waste reduction via oriented polypropylene packaging, although MAP increased the environmental loads for packaging production. Further, the proposed model could suitably predict the environmental loads of the life cycles of spinach and other products, irrespective of the packaging conditions (film, box, or tray), thereby contributing to environmentally sustainable improvements in future food-packaging systems.

The impact of foreign direct investment on innovation in China's forest products industry

The forestry industry, with its high resource dependence, long production cycles, and extensive development modes, lags behind other industries in terms of innovation. This study examines the impact of foreign direct investment (FDI) on product innovation in China's forest products industry. We use data from 146,526 forest products enterprises spanning the years 1998 to 2013, merged with patent application records from the China National Patent Database, and employ a two-way fixed effects model. Our estimates show that a 10 % increase in FDI is associated with a 0.86 % increase in patent applications, a smaller effect than in other industries. In particular, we examine how this positive effect of FDI on innovation is affected by the specific production and layout features of the forest products industry. We find that this effect is greater in regions rich in forest resources and non-coastal areas. Additionally, enterprises in this industry often focus on strategic innovations, such as product design, rather than substantial technological change, with innovation priorities differing across sub-sectors. Mechanism analyses reveal that FDI promotes innovation in forest products enterprises by increasing R&D investments, optimizing the marketing environment, and alleviating financial constraints. Extended analyses indicate significant spillover effects of FDI-driven innovation across the industry, especially between the upstream and downstream segments of the supply chain. We advise the global forest products industry to consider these unique production and layout features in their development strategies, and suggest a reorientation of government innovation policies toward long-term goals.

Distinct interspecies thermal resistance strategies exhibited by euplanktonic, tychoplanktonic and benthic diatoms under marine heatwaves

Extreme climate events, such as marine heatwaves (MHWs), are expected to occur more frequently and intensely in the future, resulting in a substantial impact on marine life. The way that diatoms respond to MHWs may have crucial effects on global primary production and biogeochemical cycles. Euplanktonic diatoms appear to benefit from MHWs directly, but this phenomenon needs an explanation. As concerns tychoplanktonic and benthic diatoms, no studies have been addressed on their thermal response strategies. To address this, we investigated the responses and underlying mechanisms of three typical growth forms of diatoms, Pseudo-nitzschia multiseries (euplanktonic), Paralia guyana (tychoplanktonic) and Navicula avium (benthic), under heat stress by combining a growth experiment with transcriptomic analysis. Our results showed that the physiological responses of diatoms to MHWs and underlying molecular mechanisms are largely related to their growth forms. The euplanktonic diatom was first depressed, but then had a distinct increase in the growth rate accompanied by inducing zeatin and unsaturated fatty acid biosynthesis and repressing substance assimilation and energy metabolism. Contrarily, the benthic diatom showed elevated substance and energy demands for macromolecules accumulation by reducing cell division and increasing photosynthesis and nitrogen assimilation. The tychoplanktonic diatom exhibited higher physiological plasticity to maintain growth and cellular homeostasis. Our results indicate the increased rate of cell division in euplanktonic diatoms under heat stress is likely an emergency response strategy promoting diatom dispersal for survival, but at the cost of disturbances of metabolic balance.

Exogenous MnSO4 Improves Productivity of Degenerated Volvariella volvacea by Regulating Antioxidant Activity

Manganese is one of the trace elements necessary for organisms to maintain normal biological activities and is also a cofactor for manganese superoxide dismutase (Mn-SOD) and manganese peroxidase (MnP). In order to find a simple and effective method to rejuvenate the degenerated V. volvacea strains, we explored the effect of the exogenous addition of MnSO4 on the antioxidant vigour and productivity of degenerated strains of V. volvacea. The results showed that the exogenous MnSO4 had no significant effect on the non-degenerated strain T0, but it could effectively increase the mycelial growth rate, mycelial biomass, and LBL decolouring ability of the degenerated strains T10 and T19, and reduce the production cycle and increased the biological efficiency of T10; it helped the severely degenerated T19 to regrow its fruiting body; and it also significantly increased the viability of the matrix-degrading enzymes such as EG, Lac, Xyl, etc. of T10 and T19. Meanwhile, exogenous MnSO4 significantly increased the activity of GPX, GR, CAT, SOD, and the content of GSH, polyphenols, minerals, and polysaccharides in T10 and T19 strains, which resulted in a significant decrease in the accumulation of ROS, such as O2− and H2O2 in T10 and T19. The correlation analysis showed that there was a significant correlation between antioxidant activity and the production ability of V. volvacea. This study can provide theoretical reference and technical support for the rejuvenation research of degenerated strains of V. volvacea and other edible fungi.

Novel deep learning based soft sensor feature extraction for part weight prediction in injection molding processes

In the current injection molding (IM) industry, it remains challenging to monitor and estimate production quality promptly. It is costly and time-consuming to measure part quality manually after each production cycle ends, which results in quality defects difficult to be captured in time. In this case, a soft sensor is essential to model the IM process and predict the final quality in real time with multi-source industrial production data. However, traditional data-driven modeling methods fail to take advantage of the information in complex high-frequency data from in-mold sensors, resulting in an inaccurate IM model and unsatisfactory quality prediction performance. To solve this problem, this paper proposes a novel soft sensor framework based on a teacher-student structure. After specialized preprocessing of multiple sensor time series data, a GRU-based autoencoder with an attention mechanism (GRU-A-AE) is trained as a teacher model, extracting deep implicit features involving valuable time sequential information. Then, a cascaded relationship among shallow feature points from sensor signals, deep features, and final part weights is established using back propagation neural networks (BPNNs). To demonstrate its effectiveness and superiority, the proposed soft sensor is trained and tested with practical IM data under normal and fluctuating production conditions, respectively. Compared with conventional methods, our method has higher prediction accuracy with testing RMSE of 0.1049 and R2 of 0.9950 under normal conditions, which proves more valuable information in high-frequency sensor signals are explored from the teacher model and IM production dynamics are captured precisely. In addition, its better prediction performance in the case of production condition fluctuation verifies its strong robustness.

ЕФЕКТИВНІСТЬ БІЗНЕС-ПРОЦЕСІВ НА ОСНОВІ ТЕХНОЛОГІЙ ОЩАДЛИВОГО ВИРОБНИЦТВА

The article examines the issue of increasing the efficiency of business processes based on Lean Production technologies. Lean production is a modern management concept aimed at minimizing losses and optimizing enterprise resources. The article compares the efficiency of implementing lean production technologies to optimize business processes at enterprises and the impact of Lean technologies on cost reduction, improvement of the quality of goods and services, and productivity improvement. In particular, the main tools of lean production, such as 5S, kanban, kaizen, standardization of work processes, and their impact on reducing costs, improving product quality, and shortening production cycles are considered. It has been established that the application of Lean technologies helps to identify and eliminate actions that do not add value to the end user, thereby improving the performance of the entire technological production system. The importance of involving personnel in the process of implementing changes and forming a corporate culture focused on continuous improvement are emphasized separately. It has been established that the introduction of Lean Production tools improves the links of the technological cycle and ensures an increase in the efficiency of business processes at the enterprise. The choice of a specific tool depends on the strategic goals of the enterprise, the industry and the level of personnel training. It has been proven that in the national business environment there is a number of barriers that prevent the introduction of lean production technologies, such as: resistance to changes in personnel and management, personnel training, limitations in financial resources, insufficient flexibility of suppliers and untimely provision of resources, as well as instability of the national economy. The results of the study indicate that the implementation of Lean Production allows to significantly increase the efficiency of business processes, reduce costs, improve product quality and increase the flexibility of the enterprise in conditions of instability of the market environment. Key words: lean production, Lean Production, business process efficiency, cost optimization, product quality, production cycles, corporate culture.

Thermal design and analysis of a fully solar-driven copper-chlorine cycle for hydrogen production

Solar copper-chlorine (Cu-Cl) thermochemical cycle is a clean, efficient and large-scale hydrogen production technology. However, when the Cu-Cl cycle is driven by solar energy, large amounts of flows with various heat-absorption and release characters at different temperatures from 25°C to 500°C, resulting in a complex internal heat exchanger network (HEN). The feasible HEN design, the heat transfer losses, and the improvement direction of the system are still unclear. Therefore, a fully solar-driven Cu-Cl cycle for hydrogen production is proposed, in which a 100 MW solar tower and an S-CO2 cycle are utilized to supply heat and electricity. A feasible HEN and possible pinch points are obtained considering energy cascade utilization. Comprehensive energy and exergy conditions are presented and the effects of direct normal irradiance (DNI), recuperative ratio (RR) and maximum temperature (Tm) are assessed. The results show that the receiver has the highest energy efficiency but the lowest exergy efficiency, and the overall system energy and exergy efficiencies are 15.93% and 16.64%. Pinch points of heating the Cu-Cl cycle and S-CO2 cycle lead to limitations of heat transfer. Furthermore, positive effects of increasing RR and Tm are significantly weakened and even turn into negative effects for increasing Tm.

Insights into carbon and nitrogen footprints of large-scale intensive pig production with different feedstuffs in China

Through the whole process of large-scale pig production, feed production puts great environmental pressure in terms of greenhouse gas (GHG) emissions and nitrogen (N) emission. Additionally, different feedstuffs will have different results. While previous studies seldom analyzed carbon footprint and nitrogen footprint from feed components or diet choices. Thus, we selected China, the largest producer and consumer of pork in the world, to analyze both nitrogen footprint and carbon footprint through the life cycle of pig production with different feed components. We used life-cycle environmental footprint and scenario analysis to compare carbon footprints, nitrogen footprints, and feed prices of large-scale pig production with different feedstuffs in China. The life cycle of the pig production includes feed crop cultivation, feed processing, pig raising, and manure management. The functional unit is the weight of 1 kg of live pig. The results showed that the carbon footprints, nitrogen footprints, and feed prices ranged from 1.67 kg CO2-eq FU−1 to 1.70 kg CO2-eq FU−1, 35.3 g Nr FU−1 to 38.9 g Nr FU−1, and 1.42 CNY kg −1 to 2.15 CNY kg −1, respectively. Feed crop production and manure management contributed the largest carbon footprint (54%) and the largest nitrogen footprint (64%), respectively. The four scenarios exhibited various results. Scenario 3 (S3), substituting soybean meal in the original feed with distillers dried grains with soluble (DDGS), presented a more favorable outcome with respect to carbon and nitrogen footprints as well as feed prices. This was mainly attributed to feed crop cultivation, manure management, crude protein contents of feeds, and prices of the feed crops. Concerning the uneven feed crop production, number of pig farrowed, feed consumption, and inter-provincial transportation across China, we conducted the spatial analysis under the optimal S3. It revealed that the northern provinces in China exhibited both higher carbon and nitrogen footprints than the southern provinces, due to the northern regions cultivating the crop feed. Finally, we proposed recommendations from perspectives of cultivation practice, feed adjustment, manure management, and strategic zoning. The study not only highlighted the importance of environmental footprint for analyzing environmental impacts of pig production, but also provided the implications for enhancing the sector’s environmental sustainability from perspective of feed adjustment.

Continuous CuCl2 Hydrolysis in the Six-Step Cu–Cl Thermochemical Cycle for Green Hydrogen Production

Continuous CuCl₂ Hydrolysis in the Six-Step Cu–Cl Thermochemical Cycle for Green Hydrogen Production

Evaluation of production line expansion efficiency using computer simulation

Abstract The article discusses the application of computer simulation in the optimization of production processes, particularly in the context of analyzing scenarios related to the addition of new production lines. The conducted research and simulations have shown that computer simulation is a key tool for precise modeling and analysis of various options, allowing for better understanding and optimization of production activities. The article presents the theoretical foundations of simulation along with practical examples of its application, focusing on assessing the impact of different production line configurations on the overall system’s efficiency. The analysis of benefits includes shortening the production cycle time, increasing flexibility, and improving operational efficiency. The challenges associated with implementing computer simulation, such as the need for specialized knowledge and the necessity for continuous updates of simulation models, are also discussed. Based on the research and analyses conducted, the article demonstrates that computer simulation is an effective tool supporting strategic and operational decision-making in production management, particularly in the context of expanding production infrastructure.

Multiomics of yaks reveals significant contribution of microbiome into host metabolism

An intensive feeding system might improve the production cycle of yaks. However, how intensive feeding system contributes to yak growth is unclear. Here, multi-omics, including rumen metagenomics, rumen and plasma metabolomics, were performed to classify the regulatory mechanisms of intensive feeding system on yaks. Increased growth performance were observed. Rumen metagenomics revealed that Clostridium, Methanobrevibacter, Piromyces and Anaeromyces increased in the intensively fed yaks, contributing to amino acid and carbohydrate metabolism. The grazing yaks had more cellulolytic microbes. These microbiomes were correlated with the pathways of “Alanine aspartate and glutamate metabolism” and “Pyruvate metabolism”. Intensive feeding increased methane degradation functions, while grazing yaks had higher methyl metabolites associated with methane production. These rumen microbiomes and their metabolites resulted in changes in plasma metabolome, finally influencing yaks’ growth. Thus, an intensive feeding system altered the rumen microbiome and metabolism as well as host metabolism, resulting in improvements of yak growth.

Ontology and production rules-based dynamic knowledge base construction methodology for machining process

With advancements in manufacturing, knowledge engineering has become important in supporting intelligent decision-making within manufacturing systems. However, existing process knowledge bases, integral to knowledge engineering, and essential for machining efficiency, product cost, and production cycles by integrating multi-source knowledge, are limited to generality, scalability, and adaptability to real production environments. These constraints undermine the application and reliability of process knowledge bases in decision-making. To overcome these challenges, an approach to constructing a dynamic machining process knowledge base (DMPKB) utilizing ontology and production rules is proposed. Firstly, a machining process knowledge model is developed by reorganizing concepts and relations to restructure process cases and experiences, thereby building a comprehensive knowledge base. Secondly, different update strategies are devised to fulfill the requirements of various components within the knowledge base. Finally, the effectiveness is validated by constructing a DMPKB for CNC boring machine bearing seats. Meanwhile, application verification is performed by generating process plans for a CNC boring machine bearing seat, showcasing the feasibility and utility of the developed knowledge base.

Studi komparatif: analisis perhitungan biaya produksi berbasis tekno-ekonomi dalam alternatif metode pembuatan telur asin

Salted eggs are a popular product, but their fragile nature requires preservation methods. One such method is salting. The production process involves direct costs (raw materials and labor) and indirect costs. Minimizing production costs is crucial for maximizing profit and shaping the company's strategy.This study aims to compares the method of curing salted eggs using red bricks and ash with salting eggs using reverse osmosis technology. Reverse osmosis method is an innovative approach in salted egg production that replaces the traditional salting method. In this method, eggs are not soaked in a salt solution but processed using high-pressure equipment to remove water from the eggs and introduce salt into them. This allows for a reduction in the time required for the salted egg production process. The result showed that the Total cost of production per one product cycle with the reverse osmosis method has a lower cost than conventional methods of IDR.257.88. The red brick method has a total cost of IDR.993,401, and the method of ash rub was the highest, with a total cost of IDR.1.017.701.Therefore, the reverse osmosis method becomes the best salting method in a cost-efficient way and has better performance.

GLN2 as a key biomarker and therapeutic target: evidence from a comprehensive pan-cancer study using molecular, functional, and bioinformatic analyses

BackgroundGNL2, a nuclear protein, is involved in ribosome production and cell cycle regulation. However, its expression and function in different types of tumors are not well understood. Comprehensive studies across multiple cancer types are needed to assess the potential of GNL2 as a diagnostic, prognostic, and immunological marker.MethodsmRNA expression data, copy number alteration threshold data, masked copy number segmentation data, and DNA methylation 450 K data from The Cancer Genome Atlas (TCGA) pan-cancer cohort were obtained from the Firehose database. Additional data, including miRNA, The Cancer Proteome Atlas (TCPA), mutation data, and clinical information, were sourced from the University of California Santa Cruz (UCSC) Xena database. The cBioPortal database facilitates the examination of GNL2 mutation frequency, location, and 3D structure in the TCGA database. Gene Expression Omnibus (GEO) data verified the transcriptome level expression in the TCGA cohort. Protein expression levels were analyzed via the Human Protein Atlas (HPA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Gene set enrichment analysis (GSEA) was employed to investigate the biological role of GNL2 across cancers. Multiple immune infiltration algorithms from the TIMER2.0 database were utilized to examine the correlation between GNL2 expression and the tumor immune microenvironment. The transcriptome-wide immune infiltration results were validated using 72 single-cell datasets from the Tumor Immune Single-cell Hub (TISCH) database. Pan-cancer survival maps were constructed, and GNL2 expression in different molecular subtypes across cancers was examined. The relationship between GNL2 and drug resistance was investigated using data from CellMiner, GDSC, and CTRP. The Comparative Toxicogenomics Database (CTD) was used to identify chemicals affecting GNL2 expression.ResultsGNL2 is located primarily in the nucleus, and its expression is regulated mainly through somatic copy number alteration (SCNA) and aberrant DNA methylation, according to TCGA data. Database analysis and immunohistochemical results from clinical samples revealed high GNL2 expression in most tumors, which was correlated with diagnostic significance. High GNL2 expression often indicates a poor prognosis with pan-cancer prognostic value. Gene set enrichment analysis (GSEA) suggested that GNL2 is involved in tumor development through cell proliferation-related pathways. GNL2 expression is correlated with the expression of immune-related genes and the infiltration levels of multiple immune cells. The relationships between GNL2 and various drugs and chemicals were examined, revealing its influence on drug sensitivity and identifying five chemicals countering GNL2-mediated pro-cancer effects.ConclusionComprehensive bioinformatics analysis of GNL2 in pan-cancer tissues, combined with experimental validation, elucidated the pan-cancer expression pattern of GNL2, determined its diagnostic and prognostic value, and explored the biological functions of GNL2. GNL2 may be involved in the regulation of cell cycle progression and remodeling of the tumor microenvironment and is associated with poor prognosis as a risk factor in most tumors. The potential of GNL2-based cancer therapies is emphasized, assisting in predicting the response to chemotherapy.

Wasserstein distributionally robust learning for predicting the cycle time of printed circuit board production

This paper proposes a Wasserstein distributionally robust learning (WDRL) model to predict the production cycle time of highly mixed printed circuit board (PCB) orders on multiple production lines. The PCB production cycle time is essential for optimizing production plans. However, the design of the PCB, production line configuration, order combinations, and personnel factors make the prediction of the PCB production cycle time difficult. In addition, practical production situations with significant disturbances in feature data make traditional prediction models inaccurate, especially when there is new data. Therefore, we establishe a WDRL model, derive a tight upper bound for the expected loss function, and reformulate a tractable equivalent model based on the bound. To demonstrate the effectiveness of this method, we collected data related to surface mounted technology (SMT) production lines from a leading global display manufacturer for our computational experiments. In addition, we also designed experiments with perturbations in the training and testing datasets to verify the WDRL model’s ability to handle perturbations. This proposed method has also been compared with other machine learning methods, such as the support vector regression combined with symbiotic organism search, decision tree, and kernel extreme learning machine, among others. Experimental results indicate that the WDRL model can make robust predictions of PCB cycle time, which helps to effectively plan production capacity in uncertain situations and avoid overproduction or underproduction. Finally, we implement the WDRL model for the actual SMT production to predict the production cycle time and set it as the target for production. We observed a 98–103 % achievement rate in the last 20 months since the implementation in September 2022.

Engineering Escherichia coli-Derived Nanoparticles for Vaccine Development.

The development of effective vaccines necessitates a delicate balance between maximizing immunogenicity and minimizing safety concerns. Subunit vaccines, while generally considered safe, often fail to elicit robust and durable immune responses. Nanotechnology presents a promising approach to address this dilemma, enabling subunit antigens to mimic critical aspects of native pathogens, such as nanoscale dimensions, geometry, and highly repetitive antigen display. Various expression systems, including Escherichia coli (E. coli), yeast, baculovirus/insect cells, and Chinese hamster ovary (CHO) cells, have been explored for the production of nanoparticle vaccines. Among these, E. coli stands out due to its cost-effectiveness, scalability, rapid production cycle, and high yields. However, the E. coli manufacturing platform faces challenges related to its unfavorable redox environment for disulfide bond formation, lack of post-translational modifications, and difficulties in achieving proper protein folding. This review focuses on molecular and protein engineering strategies to enhance protein solubility in E. coli and facilitate the in vitro reassembly of virus-like particles (VLPs). We also discuss approaches for antigen display on nanocarrier surfaces and methods to stabilize these carriers. These bioengineering approaches, in combination with advanced nanocarrier design, hold significant potential for developing highly effective and affordable E. coli-derived nanovaccines, paving the way for improved protection against a wide range of infectious diseases.

A Global Synthesis of Environmental Enrichment Effect on Fish Stress

ABSTRACTThe stress‐coping ability (SCA) is one of the core aspects of fish welfare and is of vital importance for fish production in the aquaculture industry and for fish fitness in hatchery release. Environmental enrichment (EE), a method of introducing external stimuli into the husbandry environment, has been recently proposed to improve fish SCA, but the present experimental evidence is mixed, and the reasons for these discrepancies are unclear. Here, we conducted a global meta‐analysis using a data set that consists of 1786 cases from 107 studies across 42 fish species to solve this problem. Overall, we found that enriched fish had significantly higher SCA than control fish, reflected in either basal stress levels or stress responses. Meta‐regression analyses showed that specific subgroups of enrichment type, fish developmental stage, stress category, stress duration, stress place, sample tissue and indicator system showed significant positive EE effects on SCA. Multi‐model inference indicated that the indicator system, fish developmental stage, stress characteristic and enrichment mode are important drivers for the high heterogeneity among effect sizes. These results highlight the importance of introducing EE into the rearing systems, which will not only increase the welfare of aquaculture fish but also improve the ecological adaptability of released fish. The comprehensive knowledge obtained in this analysis will provide insights into fish ontogenetic plasticity and its responses to EE and have important implications for improving the production cycle in fish aquaculture and fisheries conservation.