Search Results (158)

The circadian clock is a widespread rhythmic phenomenon across organisms, characterized by distinct gene expression patterns and behaviors at specific times of the day. Extensive genetic studies in the model fungus Neurospora crassa have yielded critical insights into the components and molecular mechanisms of circadian oscillators. However, these understandings remain absent across fungal lineages, especially from edible mushrooms. Morels (Morchella spp.) are well-recognized edible ascomycetes of considerable economic value and are partially artificially cultivated, but their biological characteristics are poorly understood. Investigating the presence of their circadian clock components, as well as the molecular underpinnings of circadian rhythms, holds important biological implications. In this study, we firstly performed a genomic search for homologs of known circadian clock genes in Morchella sextelata. Homologs of seven circadian clock genes, including wc-1, wc-2, fwd-1, frh, frq, and two additional clock-controlled genes, were identified, indicating the components necessary for the operation of a FWC oscillator contained in M. sextelata. Then, using reverse transcription quantitative PCR (RT-qPCR), the expression profiles of these seven circadian clock-related genes and four mating-type genes were examined in RNA samples which were extracted from mycelia of MAT1-1, MAT1-2 and MAT1-1 × MAT1-2 co-culture/crossed condition during conidiation under in vitro cultivation across one day. The expression levels of seven circadian clock genes and four mating-type genes displayed similar time-of-day-specific rhythmic patterns, yet remained consistently distinct across the mating-type strains and their co-culture/crossed condition, indicating a potential correlation between circadian clock and mating-type loci. Collectively, these results suggest that M. sextelata harbors conserved circadian clock-related homologs and displays mating-type-associated temporal expression differences under the tested conidiation conditions, offering a novel perspective for exploring the potential link between clock-related regulation and mating-type background in the future. Full article

Accurate estimation of soil total nitrogen (TN) and soil organic matter (SOM) is important for sustainable soil fertility assessment and precision nutrient management. Visible–near-infrared hyperspectral sensing provides a rapid and non-destructive solution, but its inversion accuracy is strongly affected by spectral preprocessing, especially under small-sample conditions. To reduce dependence on a manually selected preprocessing operator, this study proposes a homologous preprocessing representation fusion framework based on greedy concatenation (HPRF–GC). The framework constructs multiple homologous spectral views from the same raw spectrum, selects informative views through cross-validation-guided greedy forward selection, and concatenates the selected views before random forest or support vector regression. A self-built in situ hyperspectral dataset was collected from two representative black calcareous Mollisol farms in Heilongjiang Province, China, including 200 composite samples measured with a GaiaField Pro V10 imager at 5 m height under midday illumination using white reference calibration. On this dataset, HPRF–GC reduced RMSE by 3.61% for TN–RF, 9.94% for TN–SVR, 0.87% for SOM–RF, and 7.15% for SOM–SVR compared with the strongest single-preprocessing baseline, while introducing only a modest training-time overhead. On the public LUCAS 2015 dataset, HPRF–GC achieved competitive TN prediction performance, with an $R^2$ of 0.890 and an RMSE of 1.191 under RF. These results indicate that HPRF–GC provides a lightweight, interpretable and reproducible strategy for reducing preprocessing selection sensitivity in small-sample soil hyperspectral inversion. Full article

Modeling and controlling multilayer complex network dynamics is challenging under coexisting crosslayer interactions, higher-order couplings, and decentralized strategic decisions. Most existing schemes focus on graph-based pairwise structures and overlook topological cavities, mesoscale loops, and layered self-interested actions. This paper presents TopoGame-MND, an algebraic-topological and game-theoretic framework for multilayer network dynamics. We first build a filtration-driven simplicial lifting to unify pairwise and higher-order interactions into a weighted multilayer simplicial complex. A topological state operator using generalized Hodge Laplacians and persistent homology is then constructed to characterize cross-scale diffusion, circulation, and structural inconsistency. A distributed potential-game mechanism is developed with a topology-aware utility, followed by a proximal mirror-best-response algorithm with consensus correction. We prove Nash equilibrium existence and uniqueness, global potential monotone descent, linear convergence, computational complexity, and input-to-state robustness. Simulations on multiplex and interdependent networks validate that TopoGame-MND outperforms baselines in regulation speed, oscillation energy, failure resilience, and robustness, providing a unified way to connect higher-order topology and distributed decision optimization. Full article

The advancement of autonomous robotic systems has led to significant capabilities in perception, localization, mapping, and control, yet a critical challenge remains in representing and preserving relational semantics, contextual reasoning, and cognitive transparency essential for collaboration in dynamic, human-centric environments. This paper introduces a unified architecture comprising the Ontology Neural Network (ONN) and the Ontological Real-Time Semantic Fabric (ORTSF) to address this challenge. The ONN formalizes relational semantic reasoning as a dynamic topological process by embedding Forman–Ricci curvature, persistent homology, and semantic tensor structures within a unified loss formulation, aiming to maintain relational integrity as scenes evolve. Building upon ONN, the ORTSF transforms reasoning traces into actionable control commands while compensating for system delays through predictive operators designed to preserve phase margins. Theoretical analysis and extensive simulations demonstrate that ORTSF maintains designed phase margins, offering advantages over classical delay compensation methods. Empirical studies indicate the framework’s effectiveness in unifying semantic cognition and robust control, providing a mathematically principled solution for cognitive robotics. Full article

The Lovozero and Khibiny alkaline massifs (Kola Peninsula, Russian Arctic) are the prominent sources of REE minerals, with the Lovozero loparite deposit being the only currently active REE mine in Russia. A new ashcroftine-related mineral phase KA with the idealized chemical formula K₆(Na₄Ca)(Y₈Ca₃Mn)[Si₂₈O₆₈(OH)₂](CO₃)₈F₂·9H₂O was found in the Khibiny alkaline massif. Its empirical formula determined by electron microprobe analysis is Na_4.14K_6.11Ca_3.89Mn_0.59Y_6.10Ce_0.08 Gd_0.32Tb_0.15Dy_0.78Ho_0.19Er_0.35Tm_0.15Yb_0.12Lu_0.06Si₂₈C₈O_93.02F_2.08·9H₂O. The crystal structure was determined and refined by means of single-crystal X-ray diffraction analysis. The KA phase is tetragonal, I4/mmm, a = 24.1661(3), c = 17.5914(4) Å, V = 10,273.4(3) ų. The crystal structure contains two Y sites. The Y1 site is [8]-coordinated and hosts more heavy REEs, whereas the Y2 site is predominantly [7]-coordinated and accumulates lighter REEs and Mn. The crystal structure is based upon the [Si₂₈X₇₀] nanotubes (X = O,OH) elongated along the c-axis and composed of corner-sharing SiX₄ tetrahedra. The external diameter of the tubules is equal to ~19.54 Å, i.e., slightly less than 2 nm. The silicate nanotubes are running parallel to the c-axis and centered along the (00z) and (½½z) directions. The tubules are linked by walls of YO_n polyhedra that also involve triangular CO₃ groups. The K⁺, Na⁺, and Ca²⁺ cations, as well as H₂O molecules, are located either inside or outside the tubules. The crystal-chemical formula of the KA phase can be written as {K_6.14Na_4.30Ca_0.81}[Y_5.88Ca_3.12Dy_0.88Mn²⁺_0.60Gd_0.32 Ho_0.24Er_0.24Tb_0.16Tm_0.16Er_0.12Yb_0.12Ce_0.08Lu_0.08](Mn³⁺_0.09) [Si₂₈O_68.36(OH)_1.65](CO₃)₈F₂·8.97H₂O, which agrees well with the idealized formula. According to the information-based complexity analysis, the KA phase has a very complex structure and belongs to less than 3.5% of the very complex minerals known today. The presence of silicate tubules is the key reason for the exceptional structural complexity of the phase. It is impossible to establish exact relations between the KA phase and ashcroftine-(Y) on the basis of the currently available data, since the last chemical analysis of the latter mineral was done in 1924. Therefore, the mineralogical identity of ashcroftine-(Y) is currently an unresolved problem. The silicate tubule in the KA phase is topologically related to the Linde zeolite A (the LTA zeolite framework) and can be produced from the latter by a series of topological operations. The KA phase forms a homological row with caysichite-(Y) and miyawakiite-(Y), along which the Si content is increasing, and silicate chains in caysichite-(Y) transform into silicate tubules in miyawakiite-(Y) and into silicate nanotubules in the KA phase. Indeed, the M:Si:C ratio (where M = Y, REEs, Ca, Mn, Fe) changes from 1:1:0.75 for caysichite-(Y) through 0.75:1:0.5 for miyawakiite-(Y) to 0.43:1:0.29 for ashcroftine-(Y) (and KA). The increasing role of silica along the row results in the formation of zeolite-derived porous one-dimensional units. The KA phase possesses two important crystal chemical properties that distinguish it from other minerals known to date: it hosts a variety of REEs and is based upon nanoscale zeolite-like silicate units. The KA phase, ashcroftine-(Y), caysichite-(Y), and miyawakiite-(Y) have never been prepared under laboratory conditions. The mineralogical occurrence of the KA phase in the Khibiny massif points out to its secondary origin, i.e., its formation under relatively soft, low-temperature hydrothermal conditions. Thus, the discovery of the KA phase in nature may provide important hints toward its synthesis in the laboratory by means of a soft-chemistry approach. Full article

We propose an exploratory framework based on Topological Digital Twins (TDTs) for the monitoring and short-term forecasting of spatial dynamical systems. The approach represents the system through a reduced state built from topological descriptors obtained via persistent homology. These descriptors capture features such as connected components, cycles, and large-scale structure. The framework combines three components: an observation operator mapping spatial fields to a low-dimensional state, a reduced dynamical model evolving this state in time, and a data assimilation step aimed at improving robustness. This construction maps persistence diagrams to a finite-dimensional Euclidean space. This makes the model tractable but does not preserve the full algebraic structure of the original topological objects. We provide theoretical results supporting the stability of the representation under perturbations of the input field. The method is illustrated on a bow-echo convective system observed over Corsica on 18 August 2022, where the reduced state captures the main structural organization of the system over time. A comparison with standard nowcasting methods shows complementary behavior: pixel-based approaches provide better local accuracy, while the TDT framework better preserves the global spatial structure, as reflected by Wasserstein distances and persistence-based comparisons. Additional tests also indicate that the topological observables remain stable under small perturbations of the input field. The present study is based on a single case and should be understood as a proof of concept, rather than as a definitive validation. Future work will focus on validation on larger datasets and on the use of more advanced dynamical models. Full article

Short-horizon equity forecasting remains challenging because daily prices are noisy, heavy-tailed, and subject to structural breaks and regime shifts. We develop a fully automated, reproducible, and leakage-controlled multi-model pipeline for daily forecasting with horizon-specific configuration selection. The task is formulated as predicting cumulative H-day log-returns from OHLCV-derived information and converting them to implied price forecasts. All model families share a homologated design: causal feature construction, a strictly chronological split with an explicit purging rule to prevent label-window overlap for multi-day targets, training-only robustification (winsorization and adaptive clipping), and a unified metric suite computed consistently in return and price spaces. The framework benchmarks transparent baselines (zero- and mean-return), gradient-boosted trees (XGBoost), and deep temporal models (LSTM and CNN/TCN). Lookback length $L\in \{60,180,500\}$ is selected via an internal walk-forward procedure on the pre-evaluation block, and final performance is reported on an external hold-out segment (last 15% of instances). Experiments on daily data for MT, DELL, and the S&P 500 index (through 3 February 2026) show that all families achieve similarly strong price-level fit at $H=1$, largely driven by persistence in the price process, while separation across families becomes more visible at $H=5$. However, predictive performance in return space remains weak, with $R^2$ close to zero or negative, and Diebold–Mariano tests do not provide consistent evidence of statistical superiority over naive benchmarks. Under an operational rule that minimizes hold-out RMSE on the price scale, selected models are asset- and horizon-dependent, supporting horizon-wise selection rather than a single global architecture. Overall, the primary contribution lies in the proposed leakage-controlled evaluation and benchmarking framework rather than in demonstrating consistent predictive gains in financial time series forecasting. Full article

Population aging increases the demand for different assistive devices enabling independent mobility and safe vehicle boarding. This paper presents the design and development of a universal lifting platform intended to support the legs of people with reduced mobility during vehicle entry. The device was designed to be independent of a specific vehicle and to be powered by vehicle standard 12 Volt current. A CAD model of the proposed device was modeled in SolidWorks 2017 and validated through analytical calculations and finite element simulations. Based on the calculation results, a functional prototype was manufactured and tested under real operating conditions, confirming the feasibility and usability of the proposed solution. The presented platform provides a low-cost, lightweight and vehicle-independent assistive device, supporting controlled and safe leg transfer without the need for vehicle modification or homologation. Full article

DNA damage requires repair via the endonuclease IV-mediated base excision repair (BER) pathway, which corrects apurinic/apyrimidinic (AP) sites. Chlamydophila pneumoniae AP endonuclease IV (CpEndoIV), the sole AP endonuclease in this pathogen, is crucial for genomic integrity. As humans lack a homologous protein, it represents a potential therapeutic target. In this study, we report the first crystal structure of CpEndoIV at 1.97 Å resolution. The structure reveals two Zn²⁺, one Mg²⁺, and a malonate molecule bound in the active site, marking the first observation of Mg²⁺ coordination in the EndoIV family. Compared to the three-Zn²⁺ model with a narrow, deep pocket for precise AP-site cleavage, the Zn²⁺/Mg²⁺-bound state has a wider, shallower pocket that might promote diverse catalytic activities. Combined with enzymatic assays, we suggest that the mixed Zn²⁺/Mg²⁺ model is better adapted for CpEndoIV to operate under host oxidative stress. Malonate binds to the metal ions, occupying the positions normally coordinated by water molecules. This binding mode may mimic the coordination of the substrate to the metal ions, and the protein conformation resembles that of the enzyme upon substrate binding at the active site. This study provides a structural basis for the functional characterization of CpEndoIV and offers a reference for the development of targeted inhibitors against diseases caused by Chlamydophila pneumoniae. Full article

Medicine–food homology (MFH) substances, which possess both medicinal and edible properties, have garnered widespread attention in the global health context of the new era. The MFH industry has experienced explosive growth and has gradually become a key supporting aspect of TCM modernization. However, due to the pollution of the modern environment, the content of pollutants in MFH products has been increasing, raising concerns regarding quality, safety, and efficacy control. Traditional quality-analysis technologies struggle to meet the needs of rapid on-site detection because of their dependence on large instruments and the complexity of operation. This dilemma has propelled advances in sensor technology. With its advantages of high sensitivity, real-time detection, and portability, sensor technology has become a key technical support for quality control and supervision in the field of MFH. In this review, we comprehensively categorize the mainstream sensor types used for analysis in the field of MFH, including intelligent sensors, optics, electrochemistry, biosensors, etc. This review outlines their research status, elaborates on their primary application directions and corresponding core technologies, discusses current challenges (including stability, interference, and cost), and presents future perspectives. Overall, sensor-based technologies offer a promising and scalable solution for the quality control of MFH products, addressing critical challenges such as stability, interference, and cost. With ongoing advances in intelligent sensing, optics, electrochemistry, and biosensing platforms, these methods are poised to play an increasingly vital role in ensuring the safety, efficacy, and quality consistency of MFH products amid growing environmental pressures. Full article

With the advancement of global industrialization, the market for the transportation of hazardous materials is also expanding, which poses an increasingly serious threat to public safety, environmental protection, and economic stability. This study explores solutions to improve the safety and sustainability of transportation by integrating a variety of transportation modes, such as highways, railways, and waterways. We have built a comprehensive assessment system that takes into account safety considerations, operating costs, and environmental impact. The methodological contributions include an improved NSGA-II algorithm featuring population invasion and homologous competition mechanisms, combined with entropy-weighted TOPSIS for objective route selection. We use the improved NSGA-II algorithm combined with the entropy weighted TOPSIS method to model the solution, screen the optimal scheme, and determine the actual feasible route. We used the real transportation route from Berlin to Paris as a case to verify the validity of the model and proved the improved effect of the algorithm by comparing it with the baseline NSGA-II and MOQPSO. The experimental results demonstrated that the improved algorithm achieved a 133% higher hypervolume than the baseline NSGA-II and 58.8% higher than MOQPSO, while the optimal solution reduced operating costs by approximately 7.3% and carbon emissions by 12.7%. The experimental results proved that the framework effectively reduced the accident rate, operating costs, and carbon emissions. The research results provide important references for logistics planners, fully demonstrating that under the increasingly complex world pattern, it is a feasible plan to improve the efficiency of hazardous materials transportation through multimodal transportation. Full article

Lactic acid bacteria (LAB) are pivotal microorganisms in the food industry. Current approaches for functional gene validation and trait improvement in LAB primarily rely on traditional gene editing and homologous recombination techniques. These methods are often cumbersome, inefficient, and time-consuming, hindering the rapid and precise customization of strains. This limitation has, to some extent, constrained the rapid selection and industrial application of functional LAB strains. The engineering of LAB through gene editing technologies has significantly advanced both fundamental and applied research. Among these, CRISPR/Cas gene editing has successfully achieved precise modification of multiple genes in various LAB species. Compared to conventional methods, it offers superior editing efficiency and lower operational costs, opening new avenues for functional gene identification and genetic improvement in LAB. However, the application of exogenous CRISPR/Cas systems in LAB faces technical challenges such as high off-target rates, chromosomal abnormalities, and cytotoxicity. The development of endogenous CRISPR/Cas-based editing tools for LAB provides novel pathways for precise regulation, rational design, and flexible application. This paper first outlines the structural components and mechanistic principles of CRISPR/Cas gene editing tools. It then explores the research progress and applications of both endogenous and exogenous CRISPR/Cas systems in LAB. Finally, it provides an outlook on the future application of CRISPR/Cas gene editing technology in LAB, offering a reference for its implementation in this field. The advent of gene editing technologies has significantly propelled functional gene validation and trait improvement in lactic acid bacteria (LAB), thereby advancing both fundamental research and industrial applications. Notably, the CRISPR/Cas system has emerged as a transformative tool enabling precise genetic modification in diverse LAB species, offering marked improvements in editing efficiency and cost reduction relative to conventional approaches. CRISPR/Cas-based editing strategies in LAB are broadly classified into exogenous and endogenous systems. Exogenous systems operate independently of the host’s native immune repertoire, conferring the advantages of broad strain applicability and high editing efficiency. These systems have been successfully deployed for functional gene characterization, metabolic pathway engineering, such as augmenting antimicrobial production, and probiotic safety enhancement via virulence gene deletion. Conversely, endogenous systems leverage the intrinsic CRISPR/Cas machinery of LAB, offering superior biocompatibility and minimized off-target risks. Notable applications include precise gene knockout and integration using the native Type I-E system in Lacticaseibacillus paracasei. This review provides a concise overview of CRISPR/Cas system architecture and mechanisms, followed by a systematic synthesis of research progress and applications for both exogenous and endogenous systems in LAB. Finally, future directions are outlined to guide the continued development and application of CRISPR/Cas technologies in this field. Full article

Complex I (NADH:quinone oxidoreductase, CI) is central to cellular aerobic energy metabolism. The L-shaped structure of CI is unique, where the hydrophilic arm is responsible for the electron transfer function and the membrane arm operates proton pumping. These two functional sites are spatially far apart yet functionally connected. This basic core subunit architecture is highly conserved from bacterial to mammalian CI. Here, to gain detailed mechanistic insight into the role of the membrane subunit ND2 in the coupling mechanism, we mutated several highly conserved residues in the middle of the membrane axis of NuoN, the E. coli CI homolog of ND2. To more precisely investigate the consequences of mutational effects on highly conserved residues, we purified each mutant CI and compared the mutational effects on electron transfer and proton pumping activity using our instant membrane reconstitution method with E. coli double knockout (DKO) membrane vesicles lacking both CI and alternative NADH dehydrogenase (NDH-2). Thre results were corroborated by conventional proteoliposome reconstitution experiments. We found that Lys247 and Lys395 are absolutely essential for both electron transfer and proton pumping activities, while about 50% reduction of NADH oxidase activity but no reduction in proton pumping activity was observed in Lys217, and no significant decrease was detected in Glu133. Furthermore, unexpectedly, we were able to purify an NuoN knockout (ΔNuoN) mutant, which contained stoichiometric peripheral subunits NuoB, NuoCD, NuoE, NuoF, NuoG, and NuoI; and a substoichiometric amount of NuoH and a reduced amount of quinone. However, surprisingly, this isolated ΔNuoN CI showed CI activities (~30% of the WT) after being reconstituted into DKO membranes but not into proteoliposomes. Later, we confirmed by blue native PAGE that the wild-type CI was partially formed from ΔNuoN CI by recruiting its missing membrane subunits that existed in DKO membranes. Our data strongly suggest that ND2/NuoN plays an essential role in the coupling mechanism in CI. CI is the entry respiratory chain enzyme and is central to cellular energy metabolism. Two highly conserved lysine residues in the center of the antiporter-like membrane subunit ND2 are essential for the coupling mechanism between electron transfer and proton translocation. Full article

DNA cloning traditionally relies on two approaches: restriction endonuclease digestion-ligation, and homologous recombination involving exonucleases, polymerases, and other enzymes. Here, we present a novel cloning method that requires only restriction endonucleases, eliminating the need for exonucleases or polymerases. The linearized cloning vector and the foreign DNA fragment (FDF) containing overlapping sequences were mixed and incubated at the melting temperature of the overlapping DNA sequences for 5 min, then cooled slowly to 0 °C. The mixture was transformed into E. coli and positive transformants were obtained. This cloning method was named DNA ‘breathing’ recombination (DBR) cloning. The overlapping sequence between the linearized vector and the FDF is preferably from 12 to 16 base pairs. Even when the ends of the linearized vector contain mismatches of up to 20 base pairs with the ends of the FDF, the DBR cloning method can still proceed efficiently, enabling truly seamless assembly. Meanwhile, the DBR method supports one-step assembly of multiple fragments. Therefore, the DBR cloning method simplifies experimental operations and reduces experimental costs while maintaining high cloning efficiency. Full article

Electric vehicle (EV) performance can vary substantially under real-world operating conditions, particularly due to ambient temperature effects on energy consumption, battery behavior, and thermal management requirements. This study quantifies how weather conditions, daily driving patterns, and State-of-Charge (SOC) usage strategies jointly influence EV driving range, charging frequency, and overall energy efficiency. A detailed and experimentally validated Autonomie vehicle model is developed, integrating a powertrain, a mono-zonal cabin model, and a battery electro-thermal model. Three battery sizes (200-, 300-, and 400-mile homologated ranges) are assessed across five commute profiles (20–200 miles) and six ambient temperatures (−18 °C to 50 °C), including scenarios with and without preconditioning. Results show that extreme temperatures could significantly decrease the maximum achievable range by up to 55% in cold conditions (−18 °C) and 40% in hot conditions (50 °C), relative to moderate conditions. Larger battery packs retain a greater fraction of their nominal range under thermal stress, while smaller packs experience sharper relative penalties due to the higher contribution of thermal loads to total energy demand. The analysis further demonstrates that limiting operation to partial SOC windows (e.g., 80–20%), a common real-world practice, significantly reduces achievable range and increases charging frequency, particularly in cold weather. Thermal preconditioning while plugged in is shown to mitigate these effects for short trips, reducing energy consumption by up to 31% in hot conditions and 7% in cold conditions. The findings demonstrate how climate, SOC usage behavior, and thermal management jointly shape the practical driving capability of EVs, highlighting the importance of efficient thermal management and realistic user charging strategies for ensuring reliable EV operation across diverse climatic scenarios. Full article