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Route choice modelling is a critical aspect of analysing urban rail transit (URT) networks and provides a foundation for URT planning and operation. Unlike in a free-flow road network, the consideration set for route choice decisions in a URT network does not depend purely on the physical connectivity of the network and decision makers’characteristics. Instead, it is also contingent on the train schedules. This paper delves into the evolution of research on route choices in URT networks, encompassing both probabilistic route choice modelling derived from utility maximisation theory and logit curve with physical connectivity, and retrospective route choice modelling based on travel time chaining along with comprehensive transport data. The former is noted for its conciseness, simplicity, and interpretability in real-world applications, even though the methodologies may not be cutting-edge. The latter incorporates dynamic temporal information to understand activities of passengers in URT networks. Enhancements of each genres are also examined. However, these improvements might not fully address the inherent limitations of models relating to a dependency on the quality of parameters, experience of experts, and calculation efficiency. In addition, novel research adopting contemporary data mining techniques instead of classical models are introduced. The historical development of research on URT network route choices underscores the importance of amalgamating independent information networks such as surveillance networks and social networks to establish a comprehensive multi-dimensional network. Such an approach integrates passenger attributes across networks, offering a multi-dimensional understanding of passengers’ route choice behaviours. Our review work aims to present not only a systematic conceptual framework for route choices in URT networks but also a novel path for transport researchers and practitioners to decipher the travel behaviours of passengers.

Preferences can vary both across respondents (i.e. inter-respondent preference heterogeneity) and across choice tasks within respondents (i.e. intra-respondent preference heterogeneity). Ignoring the existence of intra-respondent preference heterogeneity could bias preference elicitation and demand forecast. Thus far, most studies covering inter- and intra-respondent preference heterogeneity have applied the mixed multinomial logit model. Meanwhile, the behavioural explanations for such preference variations remain under-explored. This paper accommodates inter- and intra-respondent preference heterogeneity through a two-layer latent class modelling structure, where the continuous random distributions are replaced with discrete mixtures in both layers. A latent variable representing variety-seeking is included to explain class membership probabilities, offering additional behavioural insights concerning the source of preference heterogeneity both across and within respondents. Two aspects associated with variety-seeking are examined: novelty-seeking (i.e. the inclination to adopt new modes) and alternation (i.e. the tendency to vary one’s behaviour regularly by selecting different modes continuously). In the context of new shared mobility, this paper finds the role of both aspects in preference heterogeneity. Specifically, novelty seekers are found to be more likely to fall into the class with higher probabilities of switching from existing modes to the new air taxi service than novelty avoiders, and alternation seekers are more likely to belong to the class with higher probabilities to exhibit intra-respondent preference heterogeneity than alternation avoiders. This paper, therefore, provides empirical evidence to identify the target customers of the new air taxi service.

Smart mobility has become increasingly prevalent nowadays, and new travel modes have been emerging in this process. The entry of these new modes not only fosters diversity of transport systems, but also would lead to changes of the characteristics of the transport system itself. This may induce changes in individual travel behaviour. For example, some people would shift to a new mode from other existing modes, while other individuals might be induced to make additional travel which would not be made if the new mode is not available. Some unique underlying characteristics may also drive these changes in travel behaviour. For instance, while some individuals are resistant to change, others may be prone to adopt novel options. This necessitates the investigation of the impact of variety-seeking on how people make choices when new modes are involved. Secondly, while choices are relatively stable for some individuals, others may have stronger tendencies to vary their choices more frequently over choice occasions. Exploration into this characteristic is needed to facilitate better understanding of people's consecutive choices over time. Thirdly, a new mode is usually associated with some new attributes with which individuals may be less familiar. This entails obtaining more knowledge of the role that attributes play in choice making for travel behaviour researchers. This thesis aims at examining mode choice behaviour at an individual level and uncovering travel demand through empirical analyses. Contributions are made to accounting for the three unique underlying characteristics in behaviour as mentioned above, which enhance understanding of the determinants behind mode choices and heterogeneity in preferences in the context of the introduction of new modes. This thesis exclusively uses stated preference (SP) data, as SP data can be used for preference elicitation in hypothetical scenarios, whereas it is much more difficult to collect revealed preference data when new modes have not yet been launched or have only existed in the market for a short period. This research relies on discrete choice modelling (DCM), which is a well-established econometric method for analysing individual choice behaviour and aggregate demand. DCM enables the accommodation of complex heterogeneity in preferences both across individuals and within individuals, and to achieve greater behavioural realism in delineating decision-making. The integrated choice and latent variable (ICLV) model is adopted in different manners, illustrating that the incorporation of latent variables is not confined to investigating the impact of unobserved psychological factors (e.g. variety-seeking) in choices or in class allocation, but could be extended for the purpose of combining stated choice (SC) data with other alternative SP data, e.g. best-worst scaling (BWS) data. The research findings are as expected. The study in the context of HSR (high-speed rail)-air intermodality suggests that people with stronger variety-seeking tendencies are more likely to adopt the new mode introduced. The same finding has been discovered in the second study that applies to the context where a hypothetical air taxi service is involved, which further shows that stronger variety-seeking tendencies can also lead to more unstable preferences across choices. The third study that synthesises traditional SC data and additional BWS data demonstrates the correlation between these different types of collection methods, illustrates that attributes play a relatively consistent - though not one-to-one - role across different methods, and enables the exploration of behavioural information per individual to a greater extent. In general, this thesis contributes to deeper understanding of mode choice behaviour in the context of the introduction of new modes. That is, the investigation into the impact of various level-of-service attributes provides empirical evidence for transport practitioners in willingness-to-pay evaluation. Moreover, the research indicates that while variety-seekers are more likely to be attracted to adopt a new mode at an early stage, they might in the meantime have less consistency in using the new mode. Thus, policy makers could expect an initial uptake of the new mode in the population, but it does not necessarily mean that people would keep on using the new mode over time. Furthermore, this research shows that when confronting the introduction of a new mode characterised with new attributes, an applicable approach for policy makers to improve the understanding of trade-offs and forecast of travel demand would be jointly using alternative preference elicitation methods together with the traditional SC survey.

BackgroundObsessive-compulsive disorder （OCD） is a common neuropsychiatric illness and is listed as one of the top ten disabling conditions causing loss of income and reduced quality of life. Psychological distress is an important cause of anhedonia in OCD patients， and is closely related to psychosomatic symptoms. Therefore， exploring the role of anhedonia in the relationship between psychological distress and psychosomatic symptoms is of great significance for optimizing clinical psychological treatment protocols for OCD patients.ObjectiveTo explore the role of anhedonia in the relationship between psychological distress and psychosomatic symptoms in OCD patients， with the aim of providing references for managing psychosomatic symptoms in patients.MethodsA total of 90 patients who met the diagnostic criteria for OCD according to the International Classification of Diseases， tenth edition （ICD-10）， and who visited the Mental Health Center outpatient clinic of General Hospital of Ningxia Medical University fr

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%–8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%–5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = −1.3%, range of −0.2% to −3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%–75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%–86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

As one of the most useful performance and productivity evaluation tools, the directional distance function (DDF) has received substantial attention and research. One of the key concerns to address in DDF measurement is selecting the direction along which to measure the distance from an inefficient decision making unit (DMU) to the production frontier. The least distance approach helps the inefficient DMUs find their own most preferred directions that maximize their own efficiency scores with least effort, but some DMUs may not accept the results because of the inconsistent evaluation basis. To overcome this limitation, we propose a peer-evaluation mode to evaluate the performance of the DMUs. We give a cross-directional evaluation approach and further provide a cross-bargaining game approach. In the cross-directional evaluation approach, each inefficient DMU is evaluated using both its own preferred projection direction and the other DMUs’ most preferred projection directions. However, the resulting average cross-directional efficiencies are not Pareto-optimal, so we develop a cross-bargaining game approach to improve the cross-directional efficiency approach even further. In the cross-bargaining game, each pair of inefficient DMUs is treated as two players who will obtain a common projection direction by bargaining with each other. The use of cross-bargaining negotiated projection directions and the Pareto-optimality of the DMUs’ final average cross-bargaining-directional efficiencies make the evaluation results more acceptable to all inefficient DMUs. Finally, an empirical example of 28 international airlines is applied to illustrate the practicality and superiority of our cross-bargaining game approach.

As unicellular predators, ciliates engage in close associations with diverse microbes, laying the foundation for the establishment of endosymbiosis. Originally heterotrophic, ciliates demonstrate the ability to acquire phototrophy by phagocytizing unicellular algae or by sequestering algal plastids. This adaptation enables them to gain photosynthate and develop resistance to unfavorable environmental conditions. The integration of acquired phototrophy with intrinsic phagotrophy results in a trophic mode known as mixotrophy. Additionally, ciliates can harbor thousands of bacteria in various intracellular regions, including the cytoplasm and nucleus, exhibiting species specificity. Under prolonged and specific selective pressure within hosts, bacterial endosymbionts evolve unique lifestyles and undergo particular reductions in metabolic activities. Investigating the research advancements in various endosymbiotic cases within ciliates will contribute to elucidate patterns in cellular interaction and unravel the evolutionary origins of complex traits.

Background Liver fibrosis is a progressive pathological process primarily driven by the transdifferentiation of hepatic stellate cells (HSCs) into myofibroblast-like cells which secrete excessive extracellular matrix (ECM). Although microRNAs (miRNAs) have emerged as key regulators of fibrogenesis, the therapeutic potential and mechanistic specificity of miR-214-3p (miR-214) in liver fibrosis remain insufficiently defined. Methods An adeno-associated virus (AAV)-based system was used to achieve either whole-liver or HSC-specific overexpression of miR-214 (via GFAP promoter) in a mouse model of alcohol-associated liver fibrosis induced by Lieber-DeCarli ethanol diet combined with low-dose CCl₄ injection. Liver fibrosis, steatosis, and inflammation were evaluated by biochemical assays, histology, immunostaining, and gene expression analyses. In vitro, stable miR-214 overexpression and knockdown in LX-2 cells were performed to assess effects on HSC proliferation, transdifferentiation, and ECM gene expression. MECP2 was identified as a direct functional target of miR-214 by bioinformatics and luciferase reporter assays. Results miR-214 expression was significantly downregulated during HSC activation in vitro and in fibrotic livers. Whole-liver overexpression of miR-214 alleviated liver fibrosis but caused undesirable steatosis and inflammation. Notably, HSC-specific miR-214 overexpression ameliorated liver fibrosis without inducing these adverse effects. Functionally, miR-214 inhibited HSC proliferation and ECM gene expression, while its inhibition promoted this process. Mechanistically, miR-214 exerts its anti-fibrosis function at least in part by directing targeting MECP2 , a critical regulator for HSC activation. Conclusions These findings not only identify miR-214 as a promising antifibrotic agent, but also highlight the translational advantage of cell-specific miRNA delivery. HSC-targeted miR-214 gene therapy may offer a promising and safer approach for treating liver fibrosis.

In this paper, a type of effective electronic counter-countermeasures (ECCM) technique for suppressing the high-power deception jamming using an orthogonal frequency division multiplexing (OFDM) radar is proposed. Concerning the velocity deception jamming, the initial phases of the pulses transmitted in a coherent processing interval (CPI) are designed to minimize the jamming power within a specific range, forming a notch around the jamming in the Doppler spectrum. For the purpose of suppressing the range deception jamming and the joint range-velocity deception jamming, the phase codes of the subcarriers belonging to the OFDM pulses are optimized to minimize the jamming power, distributing some specific bands in the range and the range-velocity domain, respectively. According to Parseval’s theorem, the phase encoding, acting as the coding manner of the OFDM subcarriers can ensure that the energy of each OFDM symbol stays the same. It is worth noticing that the phase codes of the OFDM subcarriers can influence the peak-to-average power ratio (PAPR). Thus, an optimization problem is formulated to optimize the phase codes of the subcarriers under the constraint of global PAPR, which can regulate the PAPRs of multiple OFDM symbols at the same time. The proposed problem is non-convex; therefore, it is a huge challenge to tackle. Then we present a method named by the phase-only alternating direction method multipliers (POADMM) to solve the aforementioned optimization problem. Some necessary simulation results are provided to demonstrate the effectiveness of the proposed radar signaling strategy

Background The role of gut microbial dysbiosis in chemotherapy-induced diarrhea (CID) pathogenesis remains unclear in humans. This study investigates gut microbiota alterations in CID patients and evaluates the therapeutic potential of probiotic supplementation. Methods To establish a paired cohort for longitudinal comparison and minimize confounding factors in assessing CID-related microbiota changes, strict inclusion/exclusion criteria were applied to gastrointestinal cancer patients. Fecal samples from eligible participants underwent shotgun metagenomic sequencing to comprehensively profile the gut microbiome composition and function. To evaluate probiotic efficacy and mechanisms, we utilized 6–8-week-old male BALB/c and C57BL/6 mice in established 5-FU- or CPT-11-induced CID models. Probiotic efficacy was assessed using primary (diarrhea severity) and secondary endpoints (body weight change, intestinal permeability). Mechanistic studies were conducted in murine models, complemented by IEC-6 cells and intestinal organoid experiments to elucidate microbiota-host interactions. Results Analysis of paired fecal samples (pre- and post-chemotherapy) from 30 gastrointestinal cancer patients ( n  = 60) revealed chemotherapy-induced reduction of Bacteroides fragilis ( B. f ) via metagenomics sequencing, with baseline B. f relative abundance negatively correlating with CID severity ( r  =  − 0.93, p  = 3.1e − 12). Building on these clinical observations, in 5-FU/CPT-11-induced CID murine models, oral gavage of heat-killed B. f ( hk-B. f ) outperformed live bacteria in diarrhea alleviation. Mechanistically, B. f -derived succinate exacerbated diarrhea, while its capsular polysaccharide (PSA) ameliorated mice diarrhea. This discovery explains the discrepant therapeutic effect between hk-B. f and live B. f . Fluorescence tracing confirmed hk-B. f transiently localized to the upper gastrointestinal tract without extraintestinal colonization. hk-B. f preserved epithelial integrity, mitochondrial function, and intestinal organoid development (higher budding count and larger organoid surface area). Moreover, hk-B. f upregulated the expression of BCL2 and downregulated the expression of BAX. Shifting the balance between BCL2 and BAX alleviates intestinal epithelial apoptosis. Caspase-3 inhibition or BCL2 silencing abrogated hk-B. f ’s anti-apoptotic effects in IEC-6 cells. Conclusions Pathological process of CID can be partially explained by compositional alterations in the gut microbiota. Supplementation with hk-B. f reduces 5-FU-stimulated epithelial injury through mitochondrial apoptotic pathway in CID murine models. These preclinical findings suggest hk-B. f merits further investigation as a potential strategy for improving CID, pending clinical validation.