[This corrects the article DOI: 10.3389/fmicb.2024.1346251.].

BACKGROUND: Energy allocation between growth and reproduction determines puberty onset and fertility. In mammals, peripheral hormones such as leptin, insulin and ghrelin signal metabolic information to the higher centres controlling gonadotrophin-releasing hormone neurone activity. However, these observations could not be confirmed in lower vertebrates, suggesting that other factors may mediate the energetic trade-off between growth and reproduction. A bioinformatic and experimental study suggested co-regulation of the circadian clock, reproductive axis and growth-regulating genes in zebrafish. While loss-of-function of most of the identified co-regulated genes had no effect or only had mild effects on reproduction, no such information existed about the co-regulated somatostatin, well-known for its actions on growth and metabolism.
RESULTS: We show that somatostatin signalling is pivotal in regulating fecundity and metabolism. Knock-out of zebrafish somatostatin 1.1 (sst1.1) and somatostatin 1.2 (sst1.2) caused a 20-30% increase in embryonic primordial germ cells, and sst1.2-/- adults laid 40% more eggs than their wild-type siblings. The sst1.1-/- and sst1.2-/- mutants had divergent metabolic phenotypes: the former had 25% more pancreatic α-cells, were hyperglycaemic and glucose intolerant, and had increased adipocyte mass; the latter had 25% more pancreatic β-cells, improved glucose clearance and reduced adipocyte mass.
CONCLUSIONS: We conclude that somatostatin signalling regulates energy metabolism and fecundity through anti-proliferative and modulatory actions on primordial germ cells, pancreatic insulin and glucagon cells and the hypothalamus. The ancient origin of the somatostatin system suggests it could act as a switch linking metabolism and reproduction across vertebrates. The results raise the possibility of applications in human and animal fertility.

Identifying the response characteristics of ecosystem service value (ESV) to changes in spatial scales, known as spatial scale effects, is crucial in guiding the development of corresponding management strategies. This paper examines ESV in China\'s terrestrial area during the year 2020, revealing the spatial aggregation characteristics of ESV and the trade-off and synergistic relationships of ecosystem services at different spatial scales, ranging from 1 km × 1 km-10 km × 10 km, with a gradient of 1 km. The results indicate: 1) The distribution pattern of ESV in China\'s terrestrial area is \"high in the southeast and low in the northwest.\" 2) The spatial characteristics of ESV in China\'s terrestrial area undergo a distinct transition at the 3 km × 3 km scale. In detail, the spatial clustering features show a trend of first rising and then falling with the increase in spatial scale, while the synergistic relationships between different ecosystem services strengthen and the trade-off relationships weaken with the increase of the spatial scale. These findings can inform the formulation of differentiated ecological protection compensation policies and enable cross-area trading of ecological values in China.

Vibrio parahaemolyticus is a food-borne pathogen, which is often isolated from various seafood products. In this study, two kinds of bacteriophages was isolated from the offshore sediments samples. The anti-phage mutant strain were obtained after seventeen rounds of co-culture of Vibrio parahaemolyticus and mixed bacteriophage, multigroup sequencing was carried out on spontaneous the anti-phage mutant strain and the wild-type strain. We used the Sanger sequencing to verify the accuracy of the mutation sites. Biolog GEN III MicroPlates were used to evaluate the metabolic capacity of wild-type strains and the anti-phage mutant strain. In this study, we found that with flaG gene (slight homology to N terminus of multiple flagellins) mutated, making the bacteriophage unable to absorb to the cell surface of the host. And, the growth competitiveness of the anti-phage mutant strain is lower than the wild-type strain. These results indicated that the fitness cost, including loss of the growth competitiveness, constitutes a barrier to the prevalence of these defense mechanisms. And the selection pressure on different anti-phage strategies depends on the trade-off between mortality imposed by bacteriophages and fitness cost of the defense strategy under the given environmental conditions. In conclusion, this study provides valuable insights into the phage-host interaction and phage resistance in Vibrio parahaemolyticus. Our study provided knowledge for the evolutionary adaption of bacteria against the bacteriophage, which could add more information to understand the phage resistance mechanism before applying in the industry.

Balancing the biomass requirements of different functions for the purpose of population reproduction and persistence can be challenging for alpine plants due to extreme environmental stresses from both above- and below-ground sources. The presence of ecosystem engineers in alpine ecosystems effectively alleviates microenvironmental stresses, hence promoting the survival and growth of other less stress-tolerant species. However, the influence of ecosystem engineers on plant resource allocation strategies remains highly unexplored. In this study, we compared resource allocation strategies, including biomass accumulation, reproductive effort (RE), root fraction (RF), as well as relationships between different functions, among four alpine plant species belonging to Gentianaceae across bare ground, tussock grass-, cushion-, and shrub-engineered microhabitats. Shrub-engineered microhabitats exerted the strongest effects on regulating plant resource allocation patterns, followed by tussock grass- and cushion-engineered microhabitats. Additionally, apart from microhabitats, population background and plant life history also significantly influenced resource allocation strategies. Generally, plants established within engineered microhabitats exhibited higher biomass accumulation, as well as increased flower, leaf and stem production. Furthermore, individuals within engineered microhabitats commonly displayed lower RF, indicating a greater allocation of resources to above-ground functions while reducing allocation to root development. RE of annual plants was significantly higher than that of perennial plants. However, individuals of annual plants within engineered microhabitats showed lower RE compared to their counterparts in bare ground habitats; whereas perennial species demonstrated similar RE between microhabitat types. Moreover, RE was generally independent of plant size in bare-ground habitats but exhibited size-dependency in certain populations for some species within specific engineered microhabitat types. However, size-dependency did exist for absolute reproductive and root biomass allocation in most of the cases examined here. No trade-offs were observed between flower mass and flower number, nor between leaf mass and leaf number. The capacity of ecosystem engineers to regulate resource allocation strategies in associated plants was confirmed. However, the resource allocation patterns resulted synergistically from the ecosystem engineering effects, population environmental backgrounds, and plant life history strategies. In general, such regulations can improve individual survival and reproductive potential, potentially promoting population persistence in challenging alpine environments.

Student dropout prediction (SDP) in educational research has gained prominence for its role in analyzing student learning behaviors through time series models. Traditional methods often focus singularly on either prediction accuracy or earliness, leading to sub-optimal interventions for at-risk students. This issue underlines the necessity for methods that effectively manage the trade-off between accuracy and earliness. Recognizing the limitations of existing methods, this study introduces a novel approach leveraging multi-objective reinforcement learning (MORL) to optimize the trade-off between prediction accuracy and earliness in SDP tasks. By framing SDP as a partial sequence classification problem, we model it through a multiple-objective Markov decision process (MOMDP), incorporating a vectorized reward function that maintains the distinctiveness of each objective, thereby preventing information loss and enabling more nuanced optimization strategies. Furthermore, we introduce an advanced envelope Q-learning technique to foster a comprehensive exploration of the solution space, aiming to identify Pareto-optimal strategies that accommodate a broader spectrum of preferences. The efficacy of our model has been rigorously validated through comprehensive evaluations on real-world MOOC datasets. These evaluations have demonstrated our model\'s superiority, outperforming existing methods in achieving optimal trade-off between accuracy and earliness, thus marking a significant advancement in the field of SDP.

The relay feedback auto-tuning method, which was an early commercialized approach, has maintained its popularity due to its simplicity and robustness. However, the classical proportional integral derivative (PID) controller auto-tuning method often results in unacceptable overshoot, especially for integrating and higher-order processes. By integrating the TID controller with the relay feedback technique, we significantly enhance dynamic control performance without relying on prior knowledge of the system. However, the direct application of the classical auto-tuning method to the TID controller encounters challenges due to the additional fractional-order transfer function s-1n. Therefore, we have developed a fractional-order Ziegler-Nichols (FOZ-N) approach, specifically designed to adjust the parameters of the TID controller. The proposed FOZ-N method is fast, simple, and capable of achieving the desired performance. In contrast to the previous Ziegler-Nichols tuning method, the TID controller parameters Kt and Ki are determined to shift the critical point (-1/Ku,j0) to the FOZ-N point (-0.5,-j0.7) on the Nyquist curve, ensuring system robustness and dynamic performance. The impact of the fractional order parameter s-1n and ratio r is explored through time-domain analysis, where these parameters are determined to ensure optimal dynamic performance. Additionally, we provide a detailed tuning procedure along with a helpful example. To demonstrate the advantages of the proposed auto-tuning TID controller over the Ziegler-Nichols PID controller, Optimal PID controller, simple internal model control (SIMC) PID controller, and Ziegler-Nichols FOPID controller, we present a simulation illustration involving multiple different systems. To validate the practical outcomes of this paper, we present experimental results on the temperature control of a Peltier cell.

Water scarcity has driven the demand for water production from unconventional sources and the reuse of industrial wastewater. Pressure-driven membranes, notably thin-film composite (TFC) membranes, stand as energy-efficient alternatives to the water scarcity challenge and various wastewater treatments. While pressure drives solvent movement, it concurrently triggers membrane compaction and flux deterioration. This necessitates a profound comprehension of the intricate interplay among compressive modulus, structural properties, and transport efficacy amid the compaction process. In this study, we present an all-encompassing compaction model for TFC membranes, applying authentic structural and mechanical variables, achieved by coupling viscoelasticity with Monte Carlo flux calculations based on the resistance-in-series model. Through validation against experimental data for multiple commercial membranes, we evaluated the influence of diverse physical parameters. We find that support polymers with a higher compressive modulus (lower compliance), supports with higher densities of \"finger-like\" pores, and \"sponge-like\" pores with optimum void fractions will be preferred to mitigate compaction. More importantly, we uncover a trade-off correlation between steady-state permeability and the modulus for identical support polymers displaying varying porosities. This model holds the potential as a valuable guide in shaping the design and optimization for further TFC applications and extending its utility to biological scaffolds and hydrogels with thin-film coatings in tissue engineering.

The polyamide (PA) nanofiltration (NF) membrane has the potential to remove endocrine-disrupting compounds (EDCs) from water and wastewater to prevent risks to both the aquatic ecosystem and human health. However, our understanding of the EDC removal-water permeance trade-off by the PA NF membrane is still limited, although the salt selectivity-water permeance trade-off has been well illustrated. This constrains the precise design of a high-performance membrane for removing EDCs. In this study, we manipulated the PA nanostructures of NF membranes by altering piperazine (PIP) monomer concentrations during the interfacial polymerization (IP) process. The upper bound coefficient for EDC selectivity-water permeance was demonstrated to be more than two magnitudes lower than that for salt selectivity-water permeance. Such variations were derived from the different membrane-solute interactions, in which the water/EDC selectivity was determined by the combined effects of steric exclusion and the hydrophobic interaction, while the electrostatic interaction and steric exclusion played crucial roles in water/salt selectivity. We further highlighted the role of the pore number and residual groups during the transport of EDC molecules across the PA membrane via molecular dynamics (MD) simulations. Fewer pores decreased the transport channels, and the existence of residual groups might cause steric hindrance and dynamic disturbance to EDC transport inside the membrane. This study elucidated the trade-off phenomenon and mechanisms between EDC selectivity and water permeance, providing a theoretical reference for the precise design of PA NF membranes for effective removal of EDCs in water reuse.

Phage therapy faces challenges against multidrug-resistant (MDR) Salmonella due to rapid phage-resistant mutant emergence. Understanding the intricate interplay between antibiotics and phages is essential for shaping Salmonella evolution and advancing phage therapy. In this study, MDR Salmonella anatum (S. anatum) 2089b coevolved with phage JNwz02 for 30 passages (60 days), then the effect of coevolution on the trade-off between phage and antibiotic resistance in bacteria was investigated. Our results demonstrated antagonistic coevolution between bacteria and phages, transitioning from arms race dynamics (ARD) to fluctuating selection dynamics (FSD). The fitness cost of phage resistance, manifested as reduced competitiveness, was observed. Bacteria evolved phage resistance while simultaneously regaining sensitivity to amoxicillin, ampicillin, and gentamicin, influenced by phage selection pressure and bacterial competitiveness. Moreover, the impact of phage selection pressure on the trade-off between antibiotic and phage resistance was more pronounced in the ARD stage than in the FSD stage. Whole genome analysis revealed mutations in the btuB gene in evolved S. anatum strains, with a notably higher mutation frequency in the ARD stage compared to the FSD stage. Subsequent knockout experiments confirmed BtuB as a receptor for phage JNwz02, and the deletion of btuB resulted in reduced bacterial competitiveness. Additionally, the mutations identified in the phage-resistant strains were linked to multiple single nucleotide polymorphisms (SNPs) associated with membrane components. This correlation implies a potential role of these SNPs in reinstating antibiotic susceptibility. These findings significantly advance our understanding of phage-host interactions and the impact of bacterial adaptations on antibiotic resistance.