Complex Transport Research Articles

Changes in RNA Splicing: A New Paradigm of Transcriptional Responses to Probiotic Action in the Mammalian Brain

Elucidating the gene regulatory mechanisms underlying the gut–brain axis is critical for uncovering novel gut–brain interaction pathways and developing therapeutic strategies for gut bacteria-associated neurological disorders. Most studies have primarily investigated how gut bacteria modulate host epigenetics and gene expression; their impact on host alternative splicing, particularly in the brain, remains largely unexplored. Here, we investigated the effects of the gut-associated probiotic Lacidofil® on alternative splicing across 10 regions of the rat brain using published RNA-sequencing data. The Lacidofil® altogether altered 2941 differential splicing events, predominantly, skipped exon (SE) and mutually exclusive exon (MXE) events. Protein–protein interactions and a KEGG analysis of differentially spliced genes (DSGs) revealed consistent enrichment in the spliceosome and vesicle transport complexes, as well as in pathways related to neurodegenerative diseases, synaptic function and plasticity, and substance addiction across brain regions. Using the PsyGeNET platform, we found that DSGs from the locus coeruleus (LConly), medial preoptic area (mPOA), and ventral dentate gyrus (venDG) were enriched in depression-associated or schizophrenia-associated genes. Notably, we highlight the App gene, where Lacidofil® precisely regulated the splicing of two exons causally involved in amyloid β protein-based neurodegenerative diseases. Although the splicing factors exhibited both splicing plasticity and expression plasticity in response to Lacidofil®, the overlap between DSGs and differentially expressed genes (DEGs) in most brain regions was rather low. Our study provides novel mechanistic insight into how gut probiotics might influence brain function through the modulation of RNA splicing.

Cholesterol metabolism regulator SREBP2 inhibits HBV replication via suppression of HBx nuclear translocation

The intricate link between cholesterol metabolism and host immune responses is well recognized, but the specific mechanisms by which cholesterol biosynthesis influences hepatitis B virus (HBV) replication remain unclear. In this study, we show that SREBP2, a key regulator of cholesterol metabolism, inhibits HBV replication by interacting directly with the HBx protein, thereby preventing its nuclear translocation. We also found that inhibiting the ER-to-Golgi transport of the SCAP-SREBP2 complex or blocking SREBP2 maturation significantly enhances HBV suppression. Notably, we demonstrate that the C-terminal domain (CTD) of SREBP2, rather than its N-terminal domain (NTD), mediates this inhibition by interacting with HBx and promoting its extracellular secretion, thus reducing nuclear HBx accumulation. These findings reveal a novel regulatory pathway that links cholesterol metabolism to HBV replication via SREBP2-mediated control of HBx localization. This insight provides a potential basis for new therapeutic strategies against HBV infection, addressing an important global health issue.

Ginkgolide B ameliorates MPTP-induced neuroinflammation and neurodegeneration by improving mitochondrial electron transport chain complex I

Although the pathology and clinical symptoms of Parkinson's disease (PD) are well-defined, the cellular and molecular mechanisms underlying the selective degeneration of dopaminergic neurons remain unclear. Mitochondrial dysfunction and neuroinflammation are increasingly recognized as central contributors to the pathogenesis of PD. The leaf extract of Ginkgolide, Ginkgo biloba, is known for its neuroprotective properties in several neurodegenerative diseases. In the present study, we sought to investigate the neuroprotective mechanism of Ginkgolide B (BN52021), a terpene lactone derived from the leaf of Ginkgo biloba, in an animal model of PD. Adult C57BL/6 mice treated with MPTP (30 mg/ kg b.wt.) exhibited significant motor deficits, ameliorated by cotreatment with BN52021 (20 mg/ Kg b.wt.), as evidenced by improved motor behaviors. MPTP administration resulted in a marked reduction in the mitochondrial complex I activity and antioxidant enzymes, specifically in the substantia nigra, whereas the striatum remained unaffected. Notably, BN52021 cotreatment restored the complex I function and antioxidant enzymes in the substantia nigra, highlighting its region-specific neuroprotective properties. Additionally, MPTP exposure significantly increased myeloperoxidase activity, a marker of oxidative stress and inflammation mitigated by BN52021. Moreover, the inflammatory markers NLRP3, MCP-1, and IL-1β were significantly upregulated following MPTP administration, indicating the activation of the inflammasome pathway. However, coadministration of MPTP with BN52021 effectively suppressed the upregulation of these inflammatory markers, suggesting a strong anti-inflammatory effect. These findings underscore the therapeutic potential of Ginkgolide in PD, primarily through its ability to enhance mitochondrial electron transport complex I activity, restore antioxidant defense, and suppress neuroinflammation.

Intelligent technologies in traffic flow control: a review

With the acceleration of global urbanization, traffic congestion has become a major bottleneck in the development of modern cities, leading to serious economic losses and a decline in the quality of life. To address this challenge, traffic flow prediction techniques have received much attention. This paper reviews the application of three key technologies, including 5G, artificial intelligence (AI), and vehicular networking (V2X) in intelligent traffic management. 5G technology provides a communication foundation with high speed rates, low latency, and large-scale device connectivity, which effectively supports real-time data transmission and communication needs. AI technology, on the other hand, with its powerful data processing capability, is able to make high-precision traffic predictions in dynamic traffic environments. However, the effectiveness of AI models is highly dependent on the support of high-quality data.V2X technology greatly improves road safety and traffic mobility by realizing real-time information exchange between vehicles and infrastructure. However, relying on a single technology alone cannot comprehensively solve complex transportation problems. For this reason, this paper proposes a technology fusion scheme of 5G, AI, and V2X, aiming to optimize the intelligence level of the traffic management system through the complementary advantages of each technology. The study shows that the synergistic application of the technologies can not only effectively alleviate traffic congestion but also improve the overall safety of roads, providing a feasible solution for the future intelligent transportation system in cities.

Measurement of Lipid Transport in Mitochondria by the MTL Complex.

Membrane biogenesis requires an extensive traffic of lipids between different cell compartments. Two main pathways, the vesicular and non-vesicular pathways, are involved in such a process. Whereas the mechanisms involved in vesicular trafficking are well understood, less is known about non-vesicular lipid trafficking, particularly in plants. This pathway involves the direct exchange of lipids at membrane contact sites (MCSs) between organelles. In plants, extensive traffic of the chloroplast-synthesized digalactosyldiacylglycerol (DGDG) to mitochondria is specifically promoted during phosphate starvation. This lipid exchange likely occurs by non-vesicular trafficking pathways at MCSs between mitochondria and plastids. By a biochemical approach, a mitochondrial lipoproteic super-complex called MTL (mitochondrial transmembrane lipoprotein complex) involved in mitochondrial lipid trafficking has been identified in Arabidopsis thaliana. This protocol describes the method used to separate the MTL complex and to study the implication of a component of this complex (AtMic60) in mitochondrial lipid transport.

Random walks with long-range memory on networks.

We study an exactly solvable random walk model with long-range memory on arbitrary networks. The walker performs unbiased random steps to nearest-neighbor nodes and intermittently resets to previously visited nodes in a preferential way such that the most visited nodes have proportionally a higher probability to be chosen for revisit. The occupation probability can be expressed as a sum over the eigenmodes of the standard random walk matrix of the network, where the amplitudes slowly decay as power-laws at large times, instead of exponentially. The stationary state is the same as in the absence of memory, and detailed balance is fulfilled. However, the relaxation of the transient part becomes critically self-organized at late times, as it is dominated by a single power-law whose exponent depends on the second largest eigenvalue and on the resetting probability. We apply our findings to finite networks, such as rings, complete graphs, Watts-Strogatz, and Barabási-Albert networks, and to Barbell and comb-like graphs. Our study could be of interest for modeling complex transport phenomena, such as human mobility, epidemic spreading, or animal foraging.

The Application of Machine Learning and Deep Learning with a Multi-Criteria Decision Analysis for Pedestrian Modeling: A Systematic Literature Review (1999–2023)

Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.

Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network

Essential genes are those whose presence is vital for a cell’s survival and growth. Detecting these genes in disease-causing organisms is critical for various biological studies, including understanding microbe metabolism, engineering genetically modified microorganisms, and identifying targets for treatment. When essential genes are expressed, they give rise to essential proteins. Identifying these genes, especially in complex organisms like Plasmodium falciparum, which causes malaria, is challenging due to the cost and time associated with experimental methods. Thus, computational approaches have emerged. Early research in this area prioritised the study of less intricate organisms, inadvertently neglecting the complexities of metabolite transport in metabolic networks. To overcome this, a Network-based Machine Learning framework was proposed. It assessed various network properties in Plasmodium falciparum, using a Genome-Scale Metabolic Model (iAM_Pf480) from the BiGG database and essentiality data from the Ogee database. The proposed approach substantially improved gene essentiality predictions as it considered the weighted and directed nature of metabolic networks and utilised network-based features, achieving a high accuracy rate of 0.85 and an AuROC of 0.7. Furthermore, this study enhanced the understanding of metabolic networks and their role in determining gene essentiality in Plasmodium falciparum. Notably, our model identified 9 genes previously considered non-essential in the Ogee database but now predicted to be essential, with some of them potentially serving as drug targets for malaria treatment, thereby opening exciting research avenues.

Reliability of Inland Water Transportation Complex Network Based on Percolation Theory: An Empirical Analysis in the Yangtze River

Inland water transportation is regarded as a crucial component of global trade, yet its reliability has been increasingly challenged by uncertainties such as extreme weather, port congestion, and geopolitical tensions. Although substantial research has focused on the structural characteristics of inland water transportation networks, the dynamic responses of these networks to disruptions remain insufficiently explored. This gap in understanding is critical for enhancing the resilience of global transportation systems as trade volumes grow and risks intensify. In this study, percolation theory was applied to evaluate the reliability of the Yangtze River transportation network. Ship voyage data from 2019 were used to construct a complex network model, and simulations of node removal were performed to identify key vulnerabilities within the network. The results showed that the failure of specific nodes significantly impacts the network’s connectivity, suggesting which nodes should be prioritized for protection. This research offers a dynamic framework for the assessment of inland water transportation network reliability and provides new insights that could guide policy decisions to improve the resilience of critical waterway systems. By identifying potential points of failure, this study contributes to the development of a more robust global trade infrastructure.

Utilizing Engineered Bacteria as "Cell Factories" In Vivo for Intracellular RNA-Loaded Outer Membrane Vesicles' Self-Assembly in Tumor Treatment.

Delivery systems play a crucial role in RNA therapy. However, the current RNA delivery system involves complex preparation and transport processes, requiring RNA preassembly in vitro, transportation at low temperatures throughout, and possibly multiple injections for improved therapeutic efficacy. To address these challenges, we developed a simple and efficient RNA delivery system. This system only requires the injection of engineered bacteria, which serve as in vivo "cell factories" for continuous production of the target RNA. The RNA can self-assemble with engineered bacteria's outer membrane vesicles (OMVs), facilitating in vivo RNA delivery. Experimental results demonstrated that this system allowed effective delivery with excellent stability and continuity for various types of RNA, including mRNA, miRNA, and siRNA. And the relative abundance of target RNA in the OMVs was 104-107 times higher than that in the mock group. We took the delivery of PD-L1 siRNA for tumor treatment as an example and found that this system could effectively downregulate the gene expression of PD-L1 by approximately twofold. Notably, a single injection of engineered bacteria achieved a significant tumor suppression of 49.37% in vivo. This research provides promising insights into the RNA delivery system for tumor therapy.

Numerical analysis of double-fractional PDEs in MHD hybrid nanofluid blood flow with slip velocity, heat source, and radiation effects

Abstract The study of blood flow in cylindrical geometries resembling small arteries is crucial for advancing drug delivery systems, cardiovascular health, and treatment methods. However, Conventional models have failed to capture the complex memory effects and non-local behavior inherent in blood flow dynamics, which hinders their accuracy in predicting critical flow and heat transfer properties for medical applications. To overcome these limitations, this research introduces a novel fractional-order magnetohydrodynamic model for blood flow, incorporating a $ZnO$ and $Fe_3O_4$ hybrid nanofluid. The model uniquely integrates boundary slip velocity effects within the double fractional Maxwell model (DFMM) rheology framework and utilizes the dual fractional phase lag bioheat model (DFPLM) applied to a porous cylindrical structure. Fractional-order time derivatives in the thermal and momentum equations are formulated using the Caputo approach, with numerical solutions derived via finite difference methods leveraging L1 and L2 approximations for Caputo fractional derivatives. The study examines the effects of fractional orders, relaxation time, and phase lags for heat and temperature, along with parameters such as thermal radiation, wall slip velocity, and porosity. These factors are analyzed for their impact on velocity, temperature, skin friction, and the Nusselt number. Results indicate that the hybrid nanofluid enhances heat transfer compared to blood or mono-hybrid nanofluids, while also reducing skin friction. Furthermore, fractional-order models provide more reliable and realistic predictions under varying flow conditions. The DFMM shows smoother transitions in velocity and friction, while the DFPLM predicts higher temperatures and greater heat transfer enhancement compared to classical and single-phase lag models. By integrating fractional calculus, this model offers improved simulation of complex transport phenomena in small arteries, contributing to the development of more effective cardiovascular treatments.

Strategic Traffic Management in Mixed Traffic Road Networks: A Methodological Approach Integrating Game Theory, Bilevel Optimization, and C-ITS

The integration of Connected Vehicles into conventional traffic systems presents significant challenges due to the diverse behaviors and objectives of different drivers. Conventional vehicle drivers typically follow User Equilibrium principles, aiming to minimize their individual travel times without considering the overall network impact. In contrast, Connected Vehicle drivers, guided by real-time information from central authorities or private service providers, can adopt System Optimum strategies or Cournot-Nash oligopoly behaviors, respectively. The coexistence of these distinct player classes in mixed-traffic environments complicates the task of achieving optimal traffic flow and network performance. This paper presents a comprehensive framework for optimizing mixed-traffic road networks through a multiclass traffic assignment model. The framework integrates three distinct types of players: conventional vehicle drivers adhering to User Equilibrium principles, Connected Vehicle drivers following System Optimum principles under a central governing authority, and Connected Vehicle drivers operating under Cournot-Nash oligopoly conditions with access to services from private companies. The methodology includes defining a model to achieve optimal mixed equilibria, designing an algorithm for multiclass traffic assignment, formulating strategic games to analyze player interactions, and establishing key performance indicators to evaluate network efficiency and effectiveness. The framework is applied to a real-world road network, validating its practicality and effectiveness through computational results. The extraction and analysis of computational results are used to propose optimal traffic management policies for mixed-traffic environments. The findings provide significant insights into the dynamics of mixed traffic networks and offer practical recommendations for improving traffic management in increasingly complex urban transportation systems.

Optimizing Stochastic Transportation Networks with Mixed Constraints Using Pareto Distribution

We propose a novel solution approach combining stochastic programming techniques with Pareto distribution characteristics. This approach involves reformulating the problem into a tractable optimization model using probabilistic constraints and employing advanced algorithms to solve the resulting mixed-integer programming problem. Numerical experiments illustrate the effectiveness of the proposed method and highlight its practical implications for transportation network design and management under uncertain conditions. The results demonstrate that incorporating Pareto-distributed uncertainties into the transportation problem provides a more realistic and adaptable framework for decision-making. The proposed solution approach offers valuable insights for managing complex transportation systems where both stochastic and deterministic factors play a crucial role.

Enzyme-Regulated Non-Thermal Fluctuations Enhance Ligand Diffusion and Receptor-Mediated Endocytosis.

Active enzymes during catalyzing chemical reactions, have been found to generate significant mechanical fluctuations, which can influence the dynamics of their surroundings. These phenomena open new avenues for controlling mass transport in complex and dynamically inhomogeneous environments through localized chemical reactions. To explore this potential, we studied the uptake of transferrin molecules in retinal pigment epithelium (RPE) cells via clathrin-mediated endocytosis. In the presence of enzyme catalysis in the extracellular matrix, we observed a significant enhancement in the transport of fluorophore-tagged transferrin inside the cells. Fluorescence correlation spectroscopy measurements showed substantial increase in transferrin diffusion in the presence of active fluctuations. This study sheds light on the possibility that enzyme-substrate reactions within the extracellular matrix may induce long-range mechanical influences, facilitating targeted material delivery within intracellular milieu more efficiently than passive diffusion. These insights are expected to contribute to the development of better therapeutic strategies by overcoming limitations imposed by slow molecular diffusion under complex environments.

Decoding complex transport patterns in flow-induced autologous chemotaxis of multicellular systems.

Cell migration via autologous chemotaxis in the presence of interstitial fluid flow is important in cancer metastasis and embryonic development. Despite significant recent progress, our understanding of flow-induced autologous chemotaxis of multicellular systems remains poor. The literature presents inconsistent findings regarding the effectiveness of collective autologous chemotaxis of densely packed cells under interstitial fluid flow. Here, we present a high-fidelity computational model to analyze the migration of multicellular systems performing autologous chemotaxis in the presence of interstitial fluid flow. Our simulations show that the details of the complex transport dynamics of the chemoattractant and fluid flow patterns that occur in the extracellular space, previously overlooked, are essential to understand this cell migration mechanism. We find that, although flow-induced autologous chemotaxis is a robust migration mechanism for individual cells, the cell-cell interactions that occur in multicellular systems render autologous chemotaxis an inefficient mechanism of collective cell migration. Our results offer new perspectives on the potential role of autologous chemotaxis in the tumor microenvironment, where fluid flow is an important modulator of transport.

Insight into the changes of European agriculture during the age of Baroque and Enlightenment: interdisciplinary survey of manor farmyard Švamberk (Czech Republic).

Following European exploration of the Americas in the late 15th century, new plants rapidly spread across Europe. Simultaneously, plants from Asia and Africa arrived. Initially, they were grown in ornamental gardens but later became integral to major production centres, significantly transforming European agriculture. Neophytes gained prominence during a period of rapid economic progress in central Europe, and many have been cultivated since the 17th century. Their importance is documented through written sources and archaeobotanical findings.This study of the manor farm Švamberk (Czechia) highlights how multidisciplinary research of agricultural production centres is crucial for understanding pre-industrial landscapes and the environmental impact of early modern societies. Agriculture's development correlates with changes in a landscape now suppressed by industrial interventions, yet key to sustainable development.Plant remains in vault infills and roofs at Švamberk farmstead were dated using dendrochronology, with 99 samples and 81,892 plant macroremains analyzed. Dendrochronological and strontium isotope analyses trace forestry and timber trade over time.Timber felled in the 17th century was likely local, but by the late 18th century, timber came via complex transportation from southern Bohemia. Primary crops were grains, oilseeds, and vegetables, with evidence of exotic species like maize, tobacco, sunflowers (native to the Americas), sorghum (native to Africa), Parthenocissus, and Chinese thuja (native to Asia), some of the oldest archaeological evidence of their cultivation in central Europe.

Modeling microplastic transport through porous media: Challenges arising from dynamic transport behavior

Modelling microplastic transport through porous media, such as soils and aquifers, is an emerging research topic, where existing hydrogeological models for (reactive) solute and colloid transport have shown limited effectiveness thus far. This perspective article draws upon recent literature to provide a brief overview of key microplastic transport processes, with emphases on less well-understood processes, to propose potential research directions for efficiently modeling microplastic transport through the porous environment. Microplastics are particulate matter with distinct physicochemical properties. Biogeochemical processes and physical interactions with the surrounding environment cause microplastic properties such as material density, geometry, chemical composition, and DLVO interaction parameters to change dynamically, through complex webs of interactions and feedbacks that dynamically affect transport behavior. Furthermore, microplastic material densities, which cluster around that of water, distinguish microplastics from other colloids, with impactful consequences that are often underappreciated. For example, (near-)neutral material densities cause microplastic transport behavior to be highly sensitive to spatio-temporally varying environmental conditions. The dynamic nature of microplastic properties implies that at environmentally relevant large spatio-temporal scales, the complex transport behavior may be effectively intractable to direct physical modeling. Therefore, efficient modeling may require integrating reduced-complexity physics-constrained models, with stochastic or statistical analyses, supported by extensive environmental data.

Two genes encoding a bacterial-type ABC transporter function in aluminum tolerance in soybean.

GmABCI5 and GmABCI13 enhance Al tolerance through regulating the composition of root cell wall, and in this process, GmABCI5 and GmABCI13 may act in the form of a complex. Aluminum (Al) toxicity is a major factor limiting plant growth in acidic soils. ATP-binding cassette (ABC) transporters are involved in plant tolerance to various environmental stresses. However, there are few reports on the ABC transporters implicated in soybean tolerance to Al toxicity. Here, we reported that two genes, GmABCI5 and GmABCI13, were involved in Al tolerance in soybean (Glycine max). GmABCI5 and GmABCI13 encode a nucleotide-binding domain and a transmembrane domain of a bacterial-type ABC transporter, respectively. The expression of both GmABCI5 and GmABCI13 was mainly induced by Al in the roots. GmABCI5 was localized at the plasma membrane and also in the cytoplasm and nucleus, while GmABCI13 was only localized at the plasma membrane. Furthermore, GmABCI5 could physically interact with GmABCI13. Overexpression of GmABCI5 or GmABCI13 in Arabidopsis reduced Al accumulation in roots and enhanced Al tolerance. However, expression of GmABCI5 and/or GmABCI13 in yeast cells did not affect Al uptake. Under Al stress, transgenic Arabidopsis lines expressing GmABCI5 or GmABCI13 had lower Al content in root cell walls than wild-type plants. Further analysis showed that Al content in cell wall fractions (pectin and hemicellulose 1) of transgenic lines was significantly lower than that of wild-type plants, which was coincident with the changes of pectin and hemicellulose 1 content under Al stress. These results indicate that GmABCI5 and GmABCI13 form an ABC transporter complex to regulate Al tolerance by affecting the modification of cell wall.

Electrochemical Study on Rate Characteristics of All-Solid-State Batteries

All-solid-state batteries (ASSBs) are next-generation batteries expected to significantly improve safety and energy density by replacing the liquid electrolyte of conventional lithium-ion batteries (LIBs) with a solid electrolyte. Although research and development for the commercialization of ASSBs are actively ongoing, many challenges remain to be addressed. In particular, the rate characteristic is directly related to the duration of discharge, which is a must-have performance for its use as a power source for transportation systems, including electric vehicles. Unlike conventional LIBs, where porous electrodes are fabricated and then filled with liquid electrolytes, ASSBs use a solid electrolyte with low fluidity, so the electrode components such as solid electrolyte, active materials, and binders are mixed at once to fabricate a composite electrode. The size, size distribution, and proportion of the solid electrolyte determine its occupancy within the composite electrode, which critically influences the lithium-ion transport through the electrolyte within the electrode. For example, increasing the proportion of active material within the electrode to achieve high energy density reduces the electrolyte content, making lithium-ion transport more difficult, leading to increased overvoltage and poor rate characteristics in the battery. In addition, during repeated cycling of ASSBs, the volume changes of the active material are not well accommodated by the solid electrolyte, inevitably causing stress at the active material | electrolyte interface. If this stress exceeds the fracture stress, the interfacial contact deteriorates. Particularly, for Ni-rich layered oxides, the most prominent cathode materials currently, rapid volume changes due to H2-H3 phase transitions at high voltage can lead to contact loss at the interface and isolation of the active materials. This increases resistance and the associated overvoltage, significantly degrading the rate characteristics of batteries. To improve the rate characteristics of ASSBs, various theoretical analyses under different design conditions of composite electrode have been attempted. However, the complexity of lithium transport within the composite electrode during high-rate charge and discharge makes it difficult to accurately simulate and understand rate characteristics. To fully understand the rate characteristics, it is necessary to non-destructively track ion transport within the electrode in real-time during battery operation. However, to the best of our knowledge, there is very few studies on this.In this study, we electrochemically investigate the lithium transport behavior inside the composite electrode during high rate operation to understand the high rate characteristics of ASSBs. For the analysis, an ASSB with a sulfide-based solid electrolyte and a Ni-rich cathode was used, and the rate performance was firstly assessed by galvanostatic rate capability test (GRCT). Incremental capacity (dQ/dV) analysis of the discharge curves at high rate discharges, where the capacity decreases rapidly, showed that the dQ/dV peak intensity significantly decreased and the peak shifted in the cathodic direction. Based on the experimental results, we proposed that the tendency of the dQ/dV curve to change with increasing rate is attributed to the incomplete reaction of the active material due to the high overvoltage during high-rate discharge. To further analyze this, the interfacial resistance was obtained by two methods, stationary electrochemical impedance spectroscopy (SEIS) under open circuit state and dynamic electrochemical impedance spectroscopy (DEIS) under various discharge rates, and the obtained resistance values were comparatively analyzed. The interfacial resistance from SEIS showed an asymmetrical U-shaped curve with a minimum value at approximately mid-level depth of discharge (DoD). On the other hand, for the interfacial resistance from DEIS, the shape of the resistance change with DoD was generally similar to that of the SEIS, but it was found that the DoD with the minimum resistance gradually shifted to higher discharge states with increasing discharge rate. The discrepancies between the results obtained in the SEIS and DEIS were discussed in terms of the uneven degree of lithiation of the active material and the resulting heterogeneity in interfacial resistance. By considering the experimental/analytical results of this study and previous theoretical analysis of high rate operation in the literature, it was proposed that kinetic limitations, such as uneven lithiation of active material and poor supply of lithium ions in the direction of the electrode depth during high discharge rate, limited the utilization of cathode, leading to a sharp drop in cell potential and degradation of rate characteristics. In this work, an electrochemical approach to analyze the rate characteristics of ASSBs will be presented, and the rate-limiting mechanisms of ASSBs will be discussed from the reaction heterogeneity perspective.

Expression of ABC transporters negatively correlates with ectoine biosynthesis in Halomonas campaniensis under NaCl and ultraviolet mutagenesis treatments revealed by transcriptomic and proteomics combined analysis

Halomonas species are renowned for their production of organic compatible solutes, particularly ectoine. However, the identification of key regulatory genes governing ectoine production in Halomonas remains limited. In this study, we conducted a combined transcriptome-proteome analysis to unveil additional regulatory genes influencing ectoine biosynthesis, particularly under ultraviolet (UV) and salt conditions. NaCl induction resulted in a 20-fold increase, while UV treatment led to at least 2.5-fold increases in ectoine production. The number of overlapping genes between transcriptomic and proteomic analyses for three comparisons, i.e., non-UV with NaCl (UV0-NaCl) vs. non-UV without NaCl (UV0), UV strain 1 (UV1-NaCl) vs. UV0-NaCl, and UV strain 2 (UV2-NaCl) vs. UV0-NaCl were 137, 19, and 21, respectively. The overlapped Gene Ontology (GO) enrichments between transcriptomic and proteomic analyses include ATPase-coupled organic phosphonate, phosphonate transmembrane transporter activity, and ATP-binding cassette (ABC) transport complex in different comparisons. Furthermore, five common genes exhibited different expression patterns at mRNA and protein levels across the three comparisons. These genes included orf01280, orf00986, orf01283, orf01282 and orf01284. qPCR verification confirmed that three of the five common genes were notably under-expressed following NaCl and UV treatments. This study highlighted the potential role of these five common genes in regulating ectoine production in Halomonas strains.