Search Results (176)

In human cells, the biogenesis of membrane proteins, which account for one quarter of polypeptides and sixty percent of human drug targets, is initiated at the membrane of the endoplasmic reticulum (ER). This process involves N-terminal signal peptides or transmembrane helices in the membrane protein precursors. Over one hundred proteins enable membrane-targeting and -insertion of the precursors as well as their folding and covalent modifications. Four targeting pathways to the Sec61 channel in the ER membrane with their effectors and three cooperating or independent membrane protein–insertases have been identified. We combined knock-down of individual components of these pathways and insertases in HeLa cells with label-free quantitative mass spectrometric analysis of the proteomes. Differential protein abundance analysis in comparison to control cells was employed to identify clients of components involved in the targeting or membrane insertion of precursors. Alternatively, knock-out cells or relevant patient fibroblasts were employed. The features of the client polypeptides were characterized to identify the client types of the different components and, ideally, their rules of engagement. In this review/article-hybrid, the focus is on global lessons from and limitations of the proteomic approach in answering the cell biological question, as well as on new aspects, such as N-terminal acetylation of membrane protein precursors. Full article

With the rapid development of urban air mobility, achieving safe and segregated flight for unmanned aerial vehicles amid the surging demand for low-altitude logistics has become a critical issue. This paper proposes a method for planning the public route network of urban low-altitude terminal logistics while considering environmental risks in the digital airspace. First, based on parallel system theory, we develop a digital airspace environment model that supports public route network planning by mapping physical and social elements to an artificial system. Furthermore, we establish a digital airspace grid partitioning system, develop grid access rules, and create a quantification model for urban low-altitude airspace environmental risks. Utilizing a layered airspace approach, this paper configures approach–departure grids, develops methods for initial public route network planning, and facilitates orderly re-planning of routes, ultimately establishing a hub-and-spoke public route network with segregation. This study conducts detailed case simulation studies based on realistic constraints, focusing on environmental risk, accurate grid configuration, comprehensive cost, algorithm complexity, and network scale. Simulation results demonstrate that the proposed method effectively constructs conflict-free networks, while maintaining low risks and inflection points. The findings align with the current development stage of urban air mobility characterized by the principle of ‘isolation first, then integration.’ This approach enables a gradual transition from route isolation to future integrated flight, thereby providing technical support for advancing low-altitude logistics operations. Full article

The sodium–citrate cotransporter (NaCT) plays a crucial role in citrate transport during amelogenesis. Mutations in the SLC13A5 gene, which encodes the NaCT, cause early infantile epileptic encephalopathy 25 and amelogenesis imperfecta. We analyzed developing pig molars and determined that the citrate concentrations in secretory- and maturation-stage enamel are both 5.3 µmol/g, with about 95% of the citrate being bound to mineral. To better understand how citrate might enter developing enamel, we developed Slc13a5^Flag reporter mice that express NaCT with a C-terminal Flag-tag (DYKDDDDK) that can be specifically and accurately recognized by commercially available anti-Flag antibodies. The 24-base Flag coding sequence was located immediately upstream of the natural translation termination codon (TAG) and was validated by Sanger sequencing. The general development, physical activities, and reproductive outcomes of this mouse strain were comparable to those of the C57BL/6 mice. No differences were detected between the Slc13a5^Flag and wild-type mice. Tooth development was extensively characterized using dissection microscopy, bSEM, light microscopy, in situ hybridization, and immunohistochemistry. Tooth formation was not altered in any detectable way by the introduction of the Flag. The Slc13a5^Flag citrate transporter was observed on all outer membranes of secretory ameloblasts (distal, lateral, and proximal), with the strongest signal on the Tomes process, and was detectable in all but the distal membrane of maturation-stage ameloblasts. The papillary layer also showed positive immunostaining for Flag. The outer membrane of odontoblasts stained stronger than ameloblasts, except for the odontoblastic processes, which did not immunostain. As NaCT is thought to only facilitate citrate entry into the cell, we performed in situ hybridization that showed Ank is not expressed by secretory- or maturation-stage ameloblasts, ruling out that ANK can transport citrate into enamel. In conclusion, we developed Slc13a5^Flag reporter mice that provide specific and sensitive localization of a fully functional NaCT-Flag protein. The localization of the Slc13a5^Flag citrate transporter throughout the ameloblast membrane suggests that either citrate enters enamel by a paracellular route or NaCT can transport citrate bidirectionally (into or out of ameloblasts) depending upon local conditions. Full article

Capsicum (pepper) is an economically vital genus in the Solanaceae family, with most species possessing about 3 Gb genomes. However, the recently sequenced Capsicum rhomboideum (~1.7 Gb) represents the first reported case of an extremely compact genome in Capsicum, providing a unique and ideal model for studying genome size evolution. To elucidate the mechanisms driving this variation, we performed comparative genomic analyses between the compact Capsicum rhomboideum and the reference Capsicum annuum cv. CM334 (~2.9 Gb). Although their genome size differences initially suggested whole-genome duplication (WGD) as a potential driver, both species shared two ancient WGD events with identical timing, predating their divergence and thus ruling out WGD as a direct contributor to their size difference. Instead, transposable elements (TEs), particularly long terminal repeat retrotransposons (LTR-RTs), emerged as the dominant force shaping genome size variation. Genome size strongly correlated with LTR-RT abundance, and multiple LTR-RT burst events aligned with major phases of genome expansion. Notably, the integrity and transcriptional activity of LTR-RTs decline over evolutionary time; older insertions exhibit greater structural degradation and reduced activity, reflecting their dynamic nature. This study systematically delineated the evolutionary trajectory of LTR-RTs—from insertion and proliferation to decay–uncovering their pivotal role in driving Capsicum genome size evolution. Our findings advance the understanding of plant genome dynamics and provide a framework for studying genome size variation across diverse plant lineages. Full article

With the continuous improvement of reservoir projects and the advancement of digital twin basin initiatives in China, rapidly and accurately generating long-term practical reservoir operation schedules has become a key priority for stakeholders. This study proposes a Theory-guided Long Short-Term Memory (TgLSTM) model to extract optimal reservoir operation rules accurately and reliably. Concretely, TgLSTM integrates data-fitting accuracy with the physical constraints of an operation, e.g., water level constraints and minimal discharge constraints, to address the low credibility often observed in conventional LSTM networks. Using the Three Gorges Reservoir during the dry season as a case study, a multi-year hydrological series optimized by particle swarm optimization (PSO) was used to train the TgLSTM network and derive optimized operation rules. Results show that TgLSTM efficiently generates operation schemes close to the theoretical optimum, achieving power generations of 4.27 × 10¹⁰ kW·h and 4.19 × 10¹⁰ kW·h in two test years, with deviations of only 4.20% and 2.33%, respectively. Compared to traditional LSTM models, TgLSTM is more reliable as it captures key operational characteristics such as terminal water levels and water level fluctuations, maintaining an average ten-day drawdown depth below 1.5 m—significantly lower than the 7 m fluctuations observed with conventional LSTM. Furthermore, comparative analyses against SwR, BP–ANN, and SVM confirm that TgLSTM offers a moderate performance in absolute metrics but is the best to simulate the constrained reservoir operation. Full article

Background: Delirium is a frequent yet pathophysiologically still poorly understood complication in the intensive care unit (ICU) and is associated with adverse outcomes for the patients. Currently, guidelines give several recommendations for treating delirium in the ICU, but to date no sufficient drug treatment exists. Dexmedetomidine, primarily used for anesthesia and sedation in ICUs has shown a preventive effect of delirium compared to other sedatives, such as propofol. We hypothesize that overnight administration of dexmedetomidine may prevent and/or shorten the duration of delirium in ICU patients. Methods: The Basel propofol dexmedetomidine (BaProDex) Study was a single-center, prospective, randomized controlled trial. We included adult ICU patients with hyperactive or mixed delirium. Patients with delirium prior to ICU admission, advanced heart block, uncontrolled hypotension, or status epilepticus were excluded. The participants were randomly assigned 1:1 to either receive dexmedetomidine (study group) or propofol (control group) as a continuous infusion overnight. The Intensive Care Delirium Screening Checklist (ICDSC) was applied at least three times per day. Delirium was defined as an ICDSC ≥ 4. The study drug was administered until the end of delirium or ICU discharge. The primary endpoint was the time to delirium episode end, which was analyzed using cumulative incidence curves and a cause specific Cox proportional hazards regression with death as a competing risk. Secondary endpoints included recurrence of delirium until 28 days after ICU discharge, death until day 28, severity of ICU delirium, number of ventilation days, ICU length of stay (LOS) in hours, hospital length of stay in days and survival after three and twelve months after ICU discharge. Due to insufficient recruitment the trial needed to be stopped prematurely. Results: In total, 38 patients were enrolled and randomized in the two groups. The median duration of delirium was shorter in the dexmedetomidine group as compared to the propofol group (ITT: 34 vs. 66 h; PP: 31 vs. 66 h), resulting in a hazard ratio of 1.92 (95% CI 0.89–4.15, p = 0.097) in the ITT and 2.95 (95% CI 1.27–6.86, p = 0.012) in the PP analysis. In the PP analysis, the 28-day mortality was lower in the dexmedetomidine group (1 vs. 5 deaths) and fewer patients needed ventilation (7 vs. 15 patients). Both ICU and hospital LOS were shorter in the dexmedetomidine group (ICU LOS: median 43 vs. 128 h; hospital LOS: median 12 vs. 22 days). Further, mortality up to three and twelve months was lower in the dexmedetomidine group compared to the propofol group (PP: 2 vs. 8 patients died within twelve months, 2 vs. 7 patients died within three months). The recurrence of delirium until 28 days after ICU discharge and severity of delirium were similar in both groups. Conclusions: Despite premature termination, BaProDex provides preliminary evidence for a reduction in the duration of delirium by nocturnal infusion of dexmedetomidine compared to propofol. Therefore, dexmedetomidine may be considered an option to treat hyperactive or mixed delirium in ICU patients. However, due to the small sample size, the study is rather of exploratory nature due to the premature termination, and we cannot rule out that the observed treatment effect is overly optimistic or by chance. Full article

The spatial separation of port yards and railway hubs, which relies on external truck drayage as a necessary link, hampers the seamless transshipment of sea–rail intermodal containers between ports and railway hubs. This creates challenges in synchronizing yard cranes (YCs) at the port terminal, external trucks (ETs) on the road, and rail-mounted gantry cranes (RMGs) at the railway hub. However, most existing studies focus on equipment scheduling or container transshipment organization under the port–railway integration mode, often overlooking critical time window constraints, such as train schedules and export container delivery deadlines. Therefore, this study investigates the collaborative scheduling of YCs, ETs, and RMGs for synchronized loading and unloading under the port–railway separation mode. A mixed-integer programming (MIP) model is developed to minimize the maximum makespan of all tasks and the empty-load time of ETs, considering practical time window constraints. Given the NP-hard complexity of this problem, an improved genetic algorithm (GA) integrated with a “First Accessible Machinery” rule is designed. Extensive numerical experiments are conducted to validate the correctness of the proposed model and the performance of the solution algorithm. The improved GA demonstrates a 6.08% better solution quality and a 97.94% reduction in computation time compared to Gurobi for small-scale instances. For medium to large-scale instances, it outperforms the adaptive large neighborhood search (ALNS) algorithm by 1.51% in solution quality and reduces computation time by 45.71%. Furthermore, the impacts of objective weights, equipment configuration schemes, port–railway distance, and time window width are analyzed to provide valuable managerial insights for decision-making to improve the overall efficiency of sea–rail intermodal systems. Full article

This paper presents an innovative optimization algorithm based on differential evolution that combines advanced mutation techniques with intelligent termination mechanisms. The proposed algorithm is designed to address the main limitations of classical differential evolution, offering improved performance for symmetric or non-symmetric optimization problems. The core scientific contribution of this research focuses on three key aspects. First, we develop a hybrid dual-strategy mutation system where the first strategy emphasizes exploration of the solution space through monitoring of the optimal solution, while the second strategy focuses on exploitation of promising regions using dynamically weighted differential terms. This dual mechanism ensures a balanced approach between discovering new solutions and improving existing ones. Second, the algorithm incorporates a novel majority dimension mechanism that evaluates candidate solutions through dimension-wise comparison with elite references (best sample and worst sample). This mechanism dynamically guides the search process by determining whether to intensify local exploitation or initiate global exploration based on majority voting across all the dimensions. Third, the work presents numerous new termination rules based on the quantitative evaluation of metric value homogeneity. These rules extend beyond traditional convergence checks by incorporating multidimensional criteria that consider both the solution distribution and evolutionary dynamics. This system enables more sophisticated and adaptive decision-making regarding the optimal stopping point of the optimization process. The methodology is validated through extensive experimental procedures covering a wide range of optimization problems. The results demonstrate significant improvements in both solution quality and computational efficiency, particularly for high-dimensional problems with numerous local optima. The research findings highlight the proposed algorithm’s potential as a high-performance tool for solving complex optimization challenges in contemporary scientific and technological contexts. Full article

Background: Mild traumatic brain injury (mTBI), commonly known as concussion, is a major public health issue characterized by subtle neuronal damage that traditional imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) often fail to detect. Fluid biomarkers have emerged as promising diagnostic and prognostic tools for mTBI. Objectives: This umbrella meta-analysis aims to evaluate the diagnostic accuracy and clinical utility of the key fluid biomarkers, S100B, glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1, neurofilament light chain (NfL)) and tau protein, in detecting mTBI and to clarify their roles as screening, confirmatory, and complementary indicators. Methods: A systematic review was performed using PubMed, Web of Science, Scopus, and Cochrane to identify the published meta-analyses that assessed the biomarkers in mTBI. Sensitivity, specificity, and diagnostic odds ratios were then calculated using random-effects models. Heterogeneity was evaluated with the I² statistic, and publication bias was assessed via funnel plots. The results of S100B demonstrated high sensitivity (91.6%) but low specificity (42.4%), making it an effective rule-out biomarker to minimize unnecessary CT scans. In contrast, GFAP exhibited moderate sensitivity (84.5%) with improved specificity (61.0%), supporting its role in confirming mTBI diagnoses. UCH-L1 revealed a sensitivity of 86.7% alongside low specificity (37.3%), indicating its potential as a complementary marker. Additionally, the NfL levels were notably elevated in sports-related concussions, while the diagnostic utility of tau protein remains inconclusive due to limited available data. Conclusions: The findings underscore the clinical promise of fluid biomarkers in the management of mTBI. S100B and GFAP are particularly valuable as screening and confirmatory markers, respectively. Nonetheless, further standardization of biomarker thresholds and additional longitudinal studies are necessary to validate the roles of UCH-L1, NfL, and Tau protein. The integration of these biomarkers into a multimodal diagnostic panel may enhance mTBI detection accuracy and facilitate improved patient stratification and management. Full article

This study proposes an unmanned combat aerial vehicle (UCAV)-oriented hierarchical reinforcement learning framework to address the temporal abstraction challenge in autonomous within-visual-range air combat (WVRAC) for UCAVs. The incorporation of maximum-entropy objectives within the MEOL framework facilitates the optimization of both autonomous low-level tactical discovery and high-level option selection. At the low level, three tactical policies (angle, snapshot, and energy tactics) are designed with reward functions informed by expert knowledge, while the high-level policy dynamically terminates current tactics and selects new ones through sparse reward learning, thus overcoming the limitations of fixed-duration tactical execution. Furthermore, a novel automatic curriculum generation mechanism based on Wasserstein Generative Adversarial Networks (WGANs) is introduced to enhance training efficiency and adaptability to diverse initial combat conditions. Extensive experiments conducted in UCAV air combat simulations have shown that MEOL not only achieves significantly better win rates than other policies when training against rule-based opponents, but also that MEOC achieves superior results in tests against tactical intra-option policies as well as other option learning policies. The framework facilitates dynamic termination and switching of tactics, thereby addressing the limitations of fixed-duration hierarchical methods. Ablation studies confirm the effectiveness of WGAN-based curricula in accelerating policy convergence. Additionally, the visual analysis of UCAVs’ flight logs validates the learned hierarchical decision-making process, showcasing the interplay between tactical selection and manoeuvring execution. This research provides novel methodologies combining hierarchical reinforcement learning with tactical domain knowledge for the autonomous decision-making of UCAVs in complex air combat scenarios. Full article

The sea–rail intermodal container terminal serves as a key transportation hub for green logistics, where efficient resource coordination directly enhances multimodal connectivity and operational synergy. To address limited storage capacity and trans-shipment inefficiencies, this study innovatively proposes a resource-sharing strategy between the seaport and the railway container terminal, focusing on the joint allocation of yard space and internal trucks. For indirect trans-shipment operations between ships, the port, the railway container terminal, and trains, a mixed-integer programming model is formulated with the objective of minimizing the container trans-shipment cost and the weighted turnaround time of ships and trains. This model simultaneously determines yard allocation, container transfers, and truck allocation. A two-layer hybrid heuristic algorithm incorporating adaptive Particle Swarm Optimization and Greedy Rules is designed. Numerical experiments verify the model and algorithm performance, revealing that the proposed method achieves an optimality gap of only 1.82% compared to CPLEX in small-scale instances while outperforming benchmark algorithms in solution quality. And the shared yard strategy enhances ship and train turnaround efficiency by an average of 33.45% over traditional storage form. Sensitivity analysis considering multiple realistic factors further confirms the robustness and generalizability. This study provides a theoretical foundation for sustainable port–railway collaboration development. Full article

“Foam container + ice pack” is a common packaging form for e-commerce logistics of litchis. However, there are numerous factors affecting the temperature variation under this logistics mode, making it difficult to control the packaging temperature and litchi quality during the e-commerce logistics process. In order to explore the impact of the packaging scheme on the packaging environment temperature and the quality variation in litchis during the “foam container + ice pack” logistics process, this paper takes the number of ice packs, the terminal pre-cooling temperature of litchis, the weight of litchis, and whether to use aluminum foil insulating film as variable factors to study the impact rules of these factors on the EPS (Expanded Polystyrene) foam container environment temperature, the total number of fruit pericarp, and the marketable fruit rate. The experimental results show the following trends: the terminal pre-cooling temperature has a significant impact on the daily average temperature of the fruit layer; the packaging environment temperature of the 15 °C pre-cooling group on the first day and the second day is 5.00 °C and 2.78 °C higher than that of the 5 °C pre-cooling experimental group, respectively. Moreover, under this treatment, the growth rate of fruit pericarp fungi is relatively fast, which could reach 3.87 Lg (CFU/g) on the second day. Increasing the amount of litchis could maintain a lower temperature environment, but it will cause the relative conductivity increasing 4.12% compared with the groups with no weight increasing. Increasing the number of ice packs could significantly reduce the decline rate of fruit soluble solids in the first two days. The research results of this paper are expected to provide a certain reference for the quality assurance logistics and the formulation of long-distance transportation strategies for perishable agricultural products. Full article

Due to external environmental factors, the layout of compound channels in estuarine ports is restricted. Moreover, with the opposing distribution of anchorages and terminals within the port, vessels navigating between these areas must cross the channel, severely affecting channel navigation safety and efficiency. In order to improve the efficiency of vessel scheduling, we analyze the layout characteristics of an estuarine port and its compound channel, summarize vessel navigation modes and traffic flow conflicts, and identify five key conflict areas. On this basis, we develop a multi-objective optimization model aimed at minimizing vessel waiting times and the total channel occupancy time ratio. This model incorporates constraints such as navigation rules, traffic flow conflicts, tidal effects, and traffic control. To solve the model, we propose an adaptive non-dominated sorting genetic algorithm, ANSGA-NS-SA, which integrates neighborhood search (NS) and Simulated Annealing (SA). The entropy-weighted technique for order preference by similarity to ideal solution (TOPSIS) is used to calculate the objective weights of the Pareto frontier and identify the optimal solution. Experimental results show that the proposed model and algorithm yield optimal port entry and exit scheduling solutions. In terms of port scheduling performance, the proposed model and algorithm outperform the traditional First-Come-First-Served (FCFS) strategy and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), reducing total vessel waiting time by 33.8% and improving total channel occupancy ratio by 8.8%. This study provides a practical and effective decision support tool for estuarine port scheduling, enhancing overall port operational efficiency. Full article

This paper describes a Krylov subspace iterative method designed for solving linear systems of equations with a large, symmetric, nonsingular, and indefinite matrix. This method is tailored to enable the evaluation of error estimates for the computed iterates. The availability of error estimates makes it possible to terminate the iterative process when the estimated error is smaller than a user-specified tolerance. The error estimates are calculated by leveraging the relationship between the iterates and Gauss-type quadrature rules. Computed examples illustrate the performance of the iterative method and the error estimates. Full article

The goal of the present study is to describe the methods used to assess the effectiveness and psychometric properties of Numetrive, a newly developed computerized adaptive testing system that measures numerical reasoning. For this purpose, an item bank was developed consisting of 174 items concurrently equated and calibrated using the two-parameter logistic model (2PLM), with item difficulties ranging between −3.4 and 2.7 and discriminations spanning from 0.51 up to 1.6. Numetrive constitutes an algorithmic combination that includes maximum likelihood estimation with fences (MLEF) for θ estimation, progressive restricted standard error (PRSE) for item selection and exposure control, and standard error of estimation as the termination rule. The newly developed CAT was evaluated in a Monte Carlo simulation study and was found to perform highly efficiently. The study demonstrated that on average 13.6 items were administered to 5000 simulees while the exposure rates remained significantly low. Additionally, the accuracy in determining the ability scores of the participants was exceptionally high as indicated by various statistical indices, including the bias statistic, mean absolute error (MAE), and root mean square error (RMSE). Finally, a validity study was performed, aimed at evaluating concurrent, convergent, and divergent validity of the newly developed CAT system. Findings verified Numertive’s robustness and applicability in the evaluation of numerical reasoning. Full article