The English spoken in China is categorised within the Expanding Circle in the World Englishes paradigm. Of late, the diversity of Chinese dialects has drawn scholarly attention to China English accents. This study contributes to the existing literature on China English by focusing on the Sichuan province in southwestern China. It involves participants from the largest Han ethnic group and the Yi minority. English monophthongs produced by 40 Sichuan Han and Yi speakers were elicited, and their distribution and contrasts were examined and compared to provide insights into the production of their English vowels. Findings from the instrumental analysis indicate a lack of vowel-quality contrasts in specific vowel pairs produced by both Han and Yi speakers, resulting in a simplified vowel system. There are notable differences between Han and Yi speakers in the production of English vowels, possibly influenced by their different first languages. In addition, specific gender-based inconsistencies were also found, suggesting gender as an influencing factor in the production of English monophthong vowels. Overall, this study identifies Sichuan English as an emerging sub-variety of China English and supports the perspective that China English is an evolving and distinct variety rather than an interlanguage.

Objectives/Goals: Mathematical models of airborne virus transmission lack supporting field and clinical data such as viral aerosol emission rates and airborne infectious doses. Here, we aim to measure inhalation exposure to influenza aerosols in a room shared with persons with community-acquired influenza and estimate the infectious dose via inhalation. Methods/Study Population: We recruited healthy volunteer recipients and influenza donors with polymerase chain reaction (PCR)-confirmed community-acquired infection. On admission to a hotel quarantine, recipients provided sera to determine baseline immunity to influenza virus, and donor infections were confirmed by quantitative real-time polymerase chain reaction. Donors and recipients were housed in separate rooms and interacted in an “event room” with controlled ventilation (0.2 – 0.5 air changes/hour) and relative humidity (20–40%). We collected ambient bioaerosol exposure samples using NIOSH BC-251 samplers. Donors provided exhaled breath samples collected by a Gesundheit-II (G-II). We analyzed aerosol samples using dPCR and fluorescent focus assays for influenza A and sera by hemagglutinin inhibition assay (HAI) against donor viruses and vaccine strains. Results/Anticipated Results: Among two cohorts (24b and 24c), we exposed 11 recipients (mean age: 36; 55% female) to 5 donors (mean age: 21; 80% female) infected with influenza A H1N1 or H3N2. Eight G-II and two NIOSH bioaerosol samples (1–4 µm and ≥4 µm) were PCR positive. We cultured virus from one G-II sample. Based on previous literature, we hypothesized that ~50% of immunologically naïve people (HAI Discussion/Significance of Impact: We demonstrated that it is feasible to recruit donors with community-acquired influenza and expose recipients to measurable virus quantities under controlled conditions. However, baseline immunity was high among volunteers. Our work sets the stage for designing studies with increased sample sizes comprising immunologically naïve volunteers.

Objectives/Goals: Lung transplant is a life-saving surgery for patients with advanced lung diseases yet long-term survival remains poor. The clinical features and lung injury patterns of lung transplant recipients who die early versus those who survive longer term remain undefined. Here, we use cell-free DNA and rejection parameters to help elucidate this further. Methods/Study Population: Lung transplant candidacy prioritizes patients who have a high mortality risk within 2 years and will likely survive beyond 5 years. We stratified patients who died within 2 years of transplant as early death (n = 50) and those who survived past 5 years as long-term survivors (n = 53). Lung transplant recipients had serial blood collected as part of two prospective cohort studies. Cell-free DNA (cfDNA) was quantified using relative (% donor-derived cfDNA {%ddcfDNA}) and absolute (nuclear-derived {n-cfDNA}, mitochondrial-derived {mt-cfDNA}) measurements. As part of routine posttransplant clinical care, all patients underwent pulmonary function testing (PFT), surveillance bronchoscopy with bronchoalveolar lavage (BAL), transbronchial biopsy (TBBx), and donor-specific antibody testing (DSA). Results/Anticipated Results: Over the first 2 years after transplant, the number of episodes of antibody-mediated rejection (p) Discussion/Significance of Impact: Clinically, early-death patients perform worse on routine surveillance PFTs and experience a worse degree of CLAD. These patients also have higher levels of cfDNA as quantified by n-cfDNA and mt-cfDNA. These results provide preliminary evidence that early-death patients have worse allograft rejection, dysfunction, and molecular injury.

Objectives/Goals: The aim of the study is to identify resistance factors for substance use (i.e., factors that explicitly help to avoid or reduce drug use). Identification of resistance factors could inform strategies that seek to reduce the prevalence of substance use and related disorders. Methods/Study Population: Adult twins aged 30–70 years were recruited from the Mid-Atlantic Twin Registry. A mixed-method approach, group concept mapping, was used to identify factors influencing participants to resist using substances. Approximately 155 participants produced 97 statements reflecting substance use resistance factors. Hierarchical cluster analysis and multidimensional scaling assessed how participants sorted and rated statements for their lifetime and current importance. Factor analysis was used to reduce data dimensionality. Reliability analyses were conducted to identify a subset of statements anticipated to consistently represent each cluster. Results were shared with participants to assess accuracy with their experiences. Results/Anticipated Results: Participants sorted 97 statements into 9 thematic clusters: (1) Controlling Personal, Negative Consequences; (2) Concern About Health and Well-being; (3) Lack of Desire; (4) Outside Influences; (5) Social Norms and My Reputation; (6) Career and Legal Impacts, (7) Avoiding Harm to Family and Relationships; (8) Preserving Family Relationships; and (9) Family and Friends Impact on Me. Participants consistently identified health concerns as an important substance use resistance factor. The statements will be further reduced to represent a smaller subset for future use as a scale to measure exposure to resistance factors. Discussion/Significance of Impact: Health concerns related to substance use were identified as an important resistance factor. This has been supported by research on smoking cessation and implemented in smoking prevention campaigns. Therefore, prioritizing health-related outcomes in prevention may be important to reduce substance use prevalence.

Psychotic disorders are known to be associated with elevated dopamine synthesis; yet, nondopamine factors may underlie the manifestation of some psychotic symptoms that are nonresponsive to dopamine-blocking agents. One under-explored nondopamine mechanism is neuroplasticity. We propose an account of the course of psychotic symptoms based on the extensive evidence for dopamine facilitation of Hebbian synaptic plasticity in cortical and subcortical memory systems. The encoding of psychotic experiences in autobiographical memory (AM) is expected to be facilitated in the hyperdopaminergic state associated with acute psychosis. However, once such ‘spurious AM of psychosis’ (SAMP) is encoded, its persistence may become dependent more on synaptic factors than dopamine factors. Under this framework, the involuntary retrieval of residual SAMP is postulated to play a key role in mediating the reactivation of symptoms with similar contents, as often observed in patients during relapse. In contrast, with active new learning of normalizing experiences across diverse real-life contexts, supported by intact dopamine-mediated salience, well-integrated SAMP may undergo ‘extinction’, leading to remission. The key steps to the integration of SAMP across psychotic and nonpsychotic memories may correspond to one’s ‘recovery style’, involving processes similar to the formation of ‘non-believed memory’ in nonclinical populations. The oversuppression of dopamine can compromise such processes. We synthesize this line of evidence into an updated dopamine-gated memory framework where neuroplasticity processes offer a parsimonious account for the recurrence, persistence, and progression of psychotic symptoms. This framework generates testable hypotheses relevant to clinical interventions.

With advancements in industrial robot technology and the ongoing enhancements in control system performance, the demand for precise robot motion is increasing. Generally, an increased number of interpolation points enhances the precision of robot movement, but excessive points can lead to jittering and out-of-step issues. This paper investigates the relationship between the number of motion interpolation points and the response times of the control system and the robot’s terminal velocity, based on the theoretical calculation and experimental analysis of the limit interpolation points for the control system of a self-developed 6-DOF (Six Degree of Freedom) robot. The method for calculating limit interpolation points is refined using the least squares method, and equations are derived for different control system response time and robot’s terminal velocity reaction times. The validity of the prediction curves is verified through experimental analysis.

An important parameter characterising the synchronisation of turbulent flows is the threshold coupling wavenumber. This study investigates the relationship between the threshold coupling wavenumber and the leading Lyapunov vector using large eddy simulations and the SABRA model. Various subgrid-scale stress models, Reynolds numbers and different coupling methods are examined. A new scaling relation is identified for the leading Lyapunov exponents in large eddy simulations, showing that they approximate those of filtered direct numerical simulations. This interpretation provides a physical basis for results related to the Lyapunov exponents of large eddy simulations, including those related to synchronisation. Synchronisation experiments show that the peak wavenumber of the energy spectrum of the leading Lyapunov vector coincides with the threshold coupling wavenumber, in large eddy simulations of box turbulence with standard Smagorinsky or dynamic mixed models as well as in the SABRA model, replicating results from direct numerical simulations of box turbulence. Although the dynamic Smagorinsky model exhibits different behaviour, the totality of the results suggests that the relationship is an intrinsic property of a certain class of chaotic systems. We also confirm that conditional Lyapunov exponents characterise the synchronisation process in indirectly coupled systems as they do in directly coupled ones, with their values insensitive to the nature of the master flow. These findings advance the understanding of the role of the Lyapunov vector in the synchronisation of turbulence.

The acoustofluidic method holds great promise for manipulating micro-organisms. When exposed to the steady vortex structures of acoustic streaming flow, these micro-organisms exhibit intriguing dynamic behaviours, such as hydrodynamic trapping and aggregation. To uncover the mechanisms behind these behaviours, we investigate the swimming dynamics of both passive and active particles within a two-dimensional acoustic streaming flow. By employing a theoretically calculated streaming flow field, we demonstrate the existence of stable bounded orbits for particles. Additionally, we introduce rotational diffusion and examine the distribution of particles under varying flow strengths. Our findings reveal that active particles can laterally migrate across streamlines and become trapped in stable bounded orbits closer to the vortex centre, whereas passive particles are confined to movement along the streamlines. We emphasise the influence of the flow field on the distribution and trapping of active particles, identifying a flow configuration that maximises their aggregation. These insights contribute to the manipulation of microswimmers and the development of innovative biological microfluidic chips.

The greatest challenge in pressure reconstruction from the measured velocity fields is that the error of material acceleration is significantly contaminated due to error propagation. Particularly for flows with moving boundaries, accurate boundary velocities are difficult to obtain due to error propagation, and a complex boundary processing technique is needed to treat the moving boundaries. The present work proposes a machine-learning-based method to determine the pressure for incompressible flows with moving boundaries. The proposed network consists of two neural networks: one network, named the boundary network, is used to track the Lagrangian boundary points; the other physics-informed neural network, named the flow network, is adopted to approximate the flow fields. These two networks are coupled by imposing boundary conditions. We further propose a new dynamic weight strategy for the loss terms to guarantee convergence and stability. The performance of the proposed method is validated by two examples: the flow over an oscillating cylinder and the flow around a swimming fish. The proposed method can accurately determine the pressure fields and boundary motion from synthetic particle image velocimetry (PIV) flow fields. Moreover, this method can also predict the boundary and pressure at a given instant without supervised data. Finally, this method was applied to reconstruct the pressure from the two-dimensional and three-dimensional PIV velocities of the left ventricle. All of the results indicate that the proposed method can accurately reconstruct the pressure fields for flows with moving boundaries and is a novel method for surface pressure estimation.

In prognosis studies with time-to-event outcomes, the survivals of groups with high/low biomarker expression are often estimated by the Kaplan–Meier method, and the difference between groups is measured by the hazard ratios (HRs). Since the high/low expressions are usually determined by study-specific cutoff values, synthesizing only HRs for summarizing the prognostic capacity of a biomarker brings heterogeneity in the meta-analysis. The time-dependent summary receiver operating characteristics (SROC) curve was proposed as a cutoff-free summary of the prognostic capacity, extended from the SROC curve in meta-analysis of diagnostic studies. However, estimates of the time-dependent SROC curve may be threatened by reporting bias in that studies with significant outcomes, such as HRs, are more likely to be published and selected in meta-analyses. Under this conjecture, this paper proposes a sensitivity analysis method for quantifying and adjusting reporting bias on the time-dependent SROC curve. We model the publication process determined by the significance of the HRs and introduce a sensitivity analysis method based on the conditional likelihood constrained by some expected proportions of published studies. Simulation studies showed that the proposed method could reduce reporting bias given the correctly-specified marginal selection probability. The proposed method is illustrated on the real-world meta-analysis of Ki67 for breast cancer.

Recent experiments and simulations have sparked growing interest in the study of Rayleigh–Bénard convection in very slender cells. One pivotal inquiry arising from this interest is the elucidation of the flow structure within these very slender cells. Here we employ tomographic particle image velocimetry, for the first time, to capture experimentally the full-field three-dimensional and three-component velocity field in a very slender cylindrical cell with aspect ratio $\Gamma =1/10$. The experiments cover a Rayleigh number range $5.0 \times 10^8 \leqslant Ra \leqslant 5.0 \times 10^9$ and Prandtl number 5.7. Our experiments reveal that the flow structure in the $\Gamma =1/10$ cell is neither in the multiple-roll form nor in the simple helical form; instead, the ascending and descending flows can intersect and cross each other, resulting in the crossing events. These crossing events separate the flow into segments; within each segment, the ascending and descending flows ascend or descend side by side vertically or in the twisting manner, and the twisting is not unidirectional, while the segments near the boundary can also be in the form of a donut like structure. By applying the mode decomposition analyses to the measured three-dimensional velocity fields, we identified the crossing events as well as the twisting events for each instantaneous flow field. Statistical analysis of the modes reveals that as $Ra$ increases, the average length of the segments becomes smaller, and the average number of segments increases from 2.5 to 3.9 in the $Ra$ range of our experiments.

When it comes to experiments with multiple-round decisions under risk, the current payoff mechanisms are incentive compatible with either outcome weighting theories or probability weighting theories, but not both. In this paper, I introduce a new payoff mechanism, the Accumulative Best Choice (“ABC”) mechanism that is incentive compatible for all rational risk preferences. I also identify three necessary and sufficient conditions for a payoff mechanism to be incentive compatible for all models of decision under risk with complete and transitive preferences. I show that ABC is the unique incentive compatible mechanism for rational risk preferences in a multiple-task setting. In addition, I test empirical validity of the ABC mechanism in the lab. The results from both a choice pattern experiment and a preference (structural) estimation experiment show that individual choices under the ABC mechanism are statistically not different from those observed with the one-round task experimental design. The ABC mechanism supports unbiased elicitation of both outcome and probability transformations as well as testing alternative decision models that do or do not include the independence axiom.