Numerous contemporary studies have posited that connected and autonomous vehicles (CAVs) hold the potential to enhance traffic safety and augment efficiency substantially. One widely discussed approach to optimize CAV operations within urban traffic networks involves the implementation of dedicated lanes (DLs). This study aims to assist system planners in optimally deploying DLs within heterogeneous urban traffic networks of CAVs and Human-Driven Vehicles (HDVs). In pursuit of this objective, we have introduced a multi-class dynamic traffic assignment framework that enhances network performance and offers insights into traffic dynamics. Additionally, our methodology considers dynamic routing behaviour while devising DLs, formulating and approximating the problem as a mixed-integer linear program (MILP). The resulting strategy delineates the temporal and spatial aspects of the deployment of DLs for CAVs, specifying the quantity and locations of these lanes. Subsequently, we assessed our framework using test-bed networks of varying sizes and demand profiles, evaluating the solution’s quality and the model’s adaptability to diverse traffic conditions. Our findings indicate that implementing DLs for CAVs can bolster vehicular throughput across the network while neglecting dynamic capacity variation in mixed traffic may yield misleading outcomes.

Numerous contemporary studies have posited that connected and autonomous vehicles (CAVs) hold the potential to enhance traffic safety and augment efficiency substantially. One widely discussed approach to optimize CAV operations within urban traffic networks involves the implementation of dedicated lanes (DLs). This study aims to assist system planners in optimally deploying DLs within heterogeneous urban traffic networks of CAVs and Human-Driven Vehicles (HDVs). In pursuit of this objective, we have introduced a multi-class dynamic traffic assignment framework that enhances network performance and offers insights into traffic dynamics. Additionally, our methodology considers dynamic routing behaviour while devising DLs, formulating and approximating the problem as a mixed-integer linear program (MILP). The resulting strategy delineates the temporal and spatial aspects of the deployment of DLs for CAVs, specifying the quantity and locations of these lanes. Subsequently, we assessed our framework using test-bed networks of varying sizes and demand profiles, evaluating the solution’s quality and the model’s adaptability to diverse traffic conditions. Our findings indicate that implementing DLs for CAVs can bolster vehicular throughput across the network while neglecting dynamic capacity variation in mixed traffic may yield misleading outcomes.

BACKGROUND: Improving tumor cell clearance by therapeutic antibodies remains a translational bottleneck because wild-type IgG1 Fc typically elicits suboptimal NK cell–mediated antibody-dependent cellular cytotoxicity (ADCC), necessitating Fc engineering to enhance activating FcγR engagement while preserving antigen specificity and manufacturability. FcγRIIIa (CD16A) is the principal activating receptor on NK cells, and its clinical relevance is underscored by the FCGR3A-158V/F polymorphism, which modulates IgG1 Fc affinity and therapeutic response. Because FcγRIIIa engagement critically depends on Fc glycosylation, microbial display platforms lacking mammalian glycan processing are limited in capturing Fc–FcγRIIIa energetics and selectivity. RESULTS: We developed a glycosylation-integrated Fc engineering platform using CHO surface display to screen glycosylated Fc libraries in a post-translationally accurate context. Multiparameter flow-cytometric selection with FcγRIIIa binding and FcγRIIb counter-screening enabled iterative isolation of PS-series Fc variants with ultra-selective, allotype-compatible FcγRIIIa recognition. Lead variants achieved 262-fold (158V) and 497-fold (158F) FcγRIIIa affinity gains while reducing FcγRIIb binding by up to 4.2-fold, expanding activating-to-inhibitory selectivity up to 2,096. This exceeded clinically deployed FcγRIIIa-enhancing Fc variants by 525-fold and 108-fold, respectively, in activating-to-inhibitory selectivity, compared with DE (S239D/I332E; used in tafasitamab) and VLPLL (L235V/F243L/R292P/Y300L/P396L; used in margetuximab). When grafted onto trastuzumab, PS variants enhanced NK cytotoxicity and improved tumor control in a trastuzumab-refractory xenograft model. Modular transfer to cetuximab and rituximab also increased cytotoxic activity. CONCLUSIONS: This study establishes a glycosylation-integrated CHO display platform for precision FcγRIIIa-targeted Fc engineering, generating modular effector domains with broad allotype compatibility, minimal FcγRIIb binding, and robust therapeutic potential in cancer immunotherapy.

Supplementary Material 1

Supplementary Material 1

This study aims to explain transit-induced commercial gentrification by quantifying the impact of opening a new metro on the closure of nearby businesses. We conducted survival analysis and Kernel Density Estimation comparing closure differences between existing and new businesses around the new metro line, focusing on periods before and after the opening. Furthermore, to identify regional heterogeneity in transit-induced commercial gentrification, we conducted a comparative analysis by dividing regions based on socioeconomic characteristics. The analysis results show that transit-induced commercial gentrification occurred around new metro stations located in areas undergoing development and socioeconomic growth and differed by land use. In particular, rapid demographic changes, an increase in commercial land price, and an increase in the overall number of businesses were observed around metro stations where commercial gentrification occurred. The framework proposed in this study is expected to be applicable to other regions and contribute to advancing the understanding of transit-induced commercial gentrification.

This study explores how psychological anxiety shaped individual transport mode choices during the early stages of the COVID-19 outbreak, prior to the implementation of nationwide restriction policies. Focusing on the intersection of public health and urban mobility, we investigate how factors such as awareness, fear, and coronaphobia influenced modal shift decisions in the absence of formal restrictions. A web-based survey was conducted in Daegu, South Korea – one of the first metropolitan areas to experience a major outbreak. Data were collected from 417 residents, covering psychological perceptions, transport usage before and after the outbreak, and preferred safety measures. We applied an ensemble classification model using the AdaBoost algorithm to predict modal shifts across various trip purposes, leveraging both sociodemographic and psychological indicators. Results show that heightened psychological anxiety led to significant reductions in public transit use, with a corresponding increase in the use of private cars, bicycles, and walking. These shifts occurred even without policy-enforced restrictions, suggesting that behavioural response to health risk perception plays a critical role in shaping urban transport demand. The study highlights the importance of integrating psychological dimensions into transport planning, particularly during crisis conditions. Findings support the development of adaptive, health-sensitive mobility strategies capable of responding not only to government interventions but also to individual psychological reactions in times of public health emergencies.HighlightsPsychological anxiety shaped urban travel choices during the coronavirus outbreak.Fear of infection reduced public transport use and encouraged safer travel modes.A two-stage framework assessed whether and how travelers changed modes.Machine learning predicted pandemic transport mode shifts with high overall accuracy.Findings support health-sensitive planning for resilient urban transport systems.

IgG-based therapeutic antibodies are increasingly adopted for diverse human diseases, such as cancer and autoimmune disorders displaying remarkable therapeutic performance. A key factor in their success lies in the extended half-life of IgG molecules, which is regulated by the pH-dependent interaction between IgG and neonatal Fc receptor (FcRn). This interaction prevents lysosomal degradation of IgG. Despite the frequent use of humanized rodent models expressing human FcRn (hFcRn) in preclinical studies, these models often fail to accurately replicate human antibody pharmacokinetics (PK) due to the use of non-native promoters that influence FcRn expression. To overcome this limitation, we developed an innovative humanized FcRn knock-in (hiFcRn) mouse model using CRISPR/Cas9 technology. This model integrates hFcRn cDNA into the endogenous locus of the mouse Fcgrt gene, completely replacing native mouse FcRn (mFcRn) expression. The hiFcRn mouse model offers a more human-relevant platform for the preclinical evaluation of therapeutic antibodies and Fc-fusion proteins.