Adverse effects of biodegradable mulch-film microplastics on Daphnia magna: Integrating energy budget, amino-acid profiling, and gene expression.

Fitness in novel environments: Bisphenol S and warming reduce energy efficiency, reproductive success, and movement across generations in zebrafish (Danio rerio).

Nano-polystyrene influences energy flow dynamics in stream brown-green food chains: Evidence from feeding preference shifts of a river snail.

Correlation between plasma enhanced high-velocity arc spraying jet characteristics and microstructure/mechanical/tribological properties of Inconel 718 coatings: The role of dual-anode energy allocation

Synergistic effect of chaperone-guided folding and energy allocation for enhancing 5-aminolevulinic acid in engineered Escherichia coli.

Thermal History Modulates PFUnDA and PFTeDA Toxicity in the Mediterranean Mussel Mytilus galloprovincialis.

Steering algal metabolic flux via EPS-mediated interfacial buffering for resilient pharmaceutical remediation in microplastic-polluted hotspots.

Horizontal transfer of mitochondria in cancer: The physiology reborn in disease?

ABSTRACT Industry 4.0 technologies are reshaping production and inventory management through real-time sensing and decision intelligence. Conventional inventory models, built on stochastic forecasts, ignore reverse logistics, carbon constraints, and energy structures, rendering them unsuitable for modern circular manufacturing. This study proposes a novel closed-loop deterministic inventory optimization model driven by IoT demand sensing, which captures real-time consumption data to eliminate forecast uncertainty and enable dynamic zero-stockout planning. The model jointly optimizes forward production and reverse remanufacturing flows, incorporating deterministic return-lag and recovery yield, renewable-grid energy allocation, carbon emissions pricing, ramping stability, and waste disposal costs. A linear trust-band mechanism preserves robustness against sensor noise by penalizing deviations from IoT measurements. The resulting scalable linear program supports real-time rolling-horizon execution. This work generalizes classical EOQ/EPQ and closed-loop formulations into a unified deterministic framework embedding sustainability, energy decarbonization, and digital supply-chain intelligence, offering actionable insights for smart, circular, low-carbon factory operations.

Biomineralization in mollusks, a fundamental process in marine ecosystems, is highly sensitive to anthropogenic stressors. Exosome-secreted species-specific shell matrix proteins (SMPs) are essential in biomineralization adaptation but remain understudied. Estuaries are considered unfavorable for biomineralization compared to open coastal zones and serve as an ideal research location to explore the roles of exosome-secreted species-specific SMPs in biomineralization adaptation under future rapid environmental change. Here, combining proteomics of shell matrix and mantle-derived exosomes, the high-abundance species-specific SMPs Car-TPM (tropomyosin from estuarine oyster Crassostrea ariakensis) and Cgi-GIGA6 (gigasin-6 from coastal oyster Crassostrea gigas) were taken as representatives to decipher the roles of exosome-secreted species-specific SMPs in oyster biomineralization adaptation. Tissue expression profiles and in situ hybridization revealed that Car-TPM was highly expressed in the adductor muscle and mantle, while Cgi-GIGA6 predominated in the mantle. Post-injury experiments demonstrated that Car-TPM expression upregulated quickly at 6h, and Cgi-GIGA6 continued to be down-regulated. Knockdown of Car-TPM suppressed shell repair, whereas silencing Cgi-GIGA6 enhanced it. In vitro assays revealed that Car-TPM significantly promoted calcium carbonate precipitation and aggregation of rhombohedral calcite crystals, whereas Cgi-GIGA6 suppressed crystallization and eroded the original flat edges. These findings indicate that Car-TPM is a positive regulator of biomineralization in C. ariakensis inhabiting harsh estuarine environments, while Cgi-GIGA6 exerts a negative regulatory effect to optimize energy allocation by restraining excessive biomineralization in C. gigas. This study reveals the essential role of species-specific SMPs secreted via exosomes in the biomineralization adaptation and adaptive potential of mollusks in future marine environments.

Energy allocation between reproduction and survival represents a fundamental life-history challenge for animals in seasonal environments. Using integrated transcriptomics and metabolomics, we investigated Nanorana taihangnica (Anura: Dicroglossidae), a non-amplexus stream frog endemic to China, to elucidate the seasonal morphological and molecular coordination between the testis and abdominal fat body. Morphological analysis showed that fat body adipocyte cross-sectional area minimized at the end of the breeding season but rapidly recovered thereafter, while testicular volume continued declining post-breeding and only recovered during the non-breeding period. During breeding season, multi-omics analyses revealed that the fat body enhanced fatty acid oxidation, upregulated histidine-carnosine metabolism, activated NAD+ metabolism and FOXO3-mediated antioxidative responses to mitigate metabolic stress, and regulated adipocyte survival and apoptosis via sphingolipid signaling. Seasonal testicular development was centrally regulated by the mTOR signaling pathway, whose activity integrated autophagy levels, NAD+ availability, and aspartate metabolism to coordinate spermatogonial proliferation and spermatogenesis. This study demonstrates that N. taihangnica optimizes seasonal energy storage, allocation, and reproductive investment through molecular and metabolic crosstalk between the fat body and testis, providing empirical insights into the physiological integration of life-history strategies in animals inhabiting fluctuating environments.

Heme oxygenase/carbon monoxide in thermoregulation and neuroenergetics.

Resting metabolic rate (RMR) underpins energy allocation to survival, reproduction, and longevity, yet its drivers in highly structured social systems remain unclear. We measured RMR in 76 naked mole-rats (Heterocephalus glaber) from five colonies to test effects of body mass, sex, reproductive status, age, social rank, and colony stability in this eusocial mammal. Body mass was the strongest predictor of RMR. Nevertheless, absolute RMR values were far below those predicted for mammals of similar mass, confirming metabolic suppression in this strictly subterranean species. Social factors produced subtler patterns. Among non-breeders, males had slightly higher (but not significantly so) RMR than females despite similar body mass. Breeding females showed elevated RMR relative to non-breeding females, but not significantly so. Age and dominance rank did not influence RMR once body mass was controlled. Colony identity explained little variation overall, although one colony showed altered metabolic scaling after social conflict, suggesting colony-level metabolic plasticity associated with hierarchical instability.

Accurate load forecasting serves as the critical factor for optimizing energy allocation and ensuring economic operation in cyber-physical power systems (CPPSs). Deep learning (DL) models have emerged as pivotal tools for capturing complex temporal patterns for enhancing the performance of load forecasting. However, the existing DL-based load forecasting models contain some shortcomings such as predominant reliance on single DL model neglecting hybrid integration potentials, manual design requiring substantial domain expertise and high model complexity lacking lightweight. To address these critical challenges, a novel multiobjective automated DL (MoADL)-based transformer-long short-term memory (TLSTM) load forecasting method is proposed to automatically design a hybrid and lightweight model by keeping the balance between model performance and model complexity, called MoADL-TLSTM. In MoADL-TLSTM, we combine Transformer's global dependency modeling with LSTM's sequential processing capabilities, enabling feature extraction from load sequences. Then, we develop the nondominated sorting genetic algorithm II (NSGA-II)-based evolutionary mechanism with thevariable-length encoding scheme and specifically designed crossover and mutation operations to represent and evolve the neural architectures (NAs) and hyper-parameters (HPs) of the TLSTM. The experiments use 12 real-world load datasets from the Australian Energy Market Operator on three Australian regions over four seasonal periods. Experimental results demonstrate the superiority of the proposed MoADL-TLSTM method to other state-of-the-art methods included manually designed single DL models, hybrid DL models, and single-objective automated DL (ADL)-based models in terms of model forecasting performance and model complexity.

Parental care increases endothermy of striped hamsters (Cricetulus barabensis) rearing different litter size.

Acoustic behavior, essential for communication and perception, is metabolically demanding. Studying the energy costs of echolocation helps us to understand animal energy allocation and provides key insights into the evolutionary constraints of acoustic signals. We examined the constant-frequency bat Rhinolophus nippon using a miniature electrocardiogram system and a custom servomotor that moved prey toward stationary bats. This setup allowed for synchronous recording of high-resolution electrocardiogram and echolocation calls from the search phase to the approach phase. During the search phase, bats emitted isolated echolocation pulses characterized by long pulse durations and inter-pulse intervals (IPIs), together with higher root mean square (RMS) amplitude, pulse energy, and peak amplitude. In the approach phase, call rate increased significantly (3.15-fold), and bats predominantly produced sonar sound groups. Meanwhile, pulse duration, IPIs, RMS amplitude, and pulse energy decreased to 65.23%, 25.82%, 78.50%, and 86.32% of the corresponding search-phase values, whereas peak amplitude increased to 110.99%, indicating that R. nippon can flexibly adjust the structure of its echolocation calls. However, despite the increased call rate (p < 0.05), neither heart rate nor metabolic rate differed between phases. This study provides direct physiological evidence for understanding energy expenditure in bat echolocation and offers a methodological reference for future research.

Physiological and proteomic traits underlying differential growth in the freshwater crayfish Cherax quadricarinatus.

ABSTRACTHorse mackerel (Trachurus trachurus, Linnaeus, 1758) is an epipelagic fish commonly found in Moroccan Mediterranean waters. This study was aimed at studying the ecology by monitoring gonadal development and reproductive indices of T. trachurus populations along the eastern Moroccan Mediterranean coast over a 12‐month period. For that, a total of 390 specimens were sampled, with total lengths ranging from 7.8 to 33.8 cm and weights from 3.78 to 310.52 g. Several reproductive indices were analyzed. Monthly monitoring of the gonado‐somatic index (GSI) identified two spawning periods for T. trachurus: the first in late winter‐early spring (February–April), and the second in summer (July–September), confirming that this species is a fractional spawner fish. The hepato‐somatic index (HSI) followed a similar trend to GSI, reflecting the energy allocation for reproduction. The size at first sexual maturity (L50) was estimated at 23.5 cm for females and 22.5 cm for males, representing critical metrics for fisheries management, as they help set regulatory guidelines, such as minimum landing sizes, and are also a prerequisite for estimating spawning stock biomass. The overall sex ratio showed a predominance in favor of males (62%) over females (38%). These results represent a valuable contribution to a better understanding of the biology of T. trachurus, and to the implementation of stock assessment to ensure the sustainability of catches.

There is currently no theory which mechanistically links parasite virulence and host senescence. One driver of senescence is energy metabolism. We expect parasites to contribute to senescence because infection increases energy allocation to the immune system, which elevates oxidative stress and damages macromolecules. Additionally, parasites deplete their host's energy, which can increase the host's rate of mortality. While these within-host processes can contribute to mortality, they also determine the risk of parasite infection through epidemiological dynamics. To understand quantitatively the impact of these interactions on infection dynamics and on the evolution of within-host parasite growth (virulence), we developed a multiscale model that describes within-host and among-host dynamics. We found that infection prevalence is maximised at intermediate levels of resource acquisition only when energy depletion contributes to senescence. Furthermore, for a single resource level, there are two evolutionary equilibria for the parasites' growth rate. This is the first time the molecular mechanisms determining senescence have been considered in the context of parasite infection, allowing us to link virulence and senescence. Our mechanistic framework provides an alternative to the virulence-transmission trade-off and reconciles conflicting empirical findings on the impact of resources on epidemiological dynamics and virulence.

Deciphering the impact of light-dark cycles on the interaction between cyanophage YongM and Nostoc sp. FACHB-596.