| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
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| 3. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 4. |
- Cao, Qi, et al.
(författare)
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Jointly estimating the most likely driving paths and destination locations with incomplete vehicular trajectory data
- 2023
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Ingår i: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 155
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Tidskriftsartikel (refereegranskat)abstract
- With an ever-increasing deployment density of probe and fixed sensors, massive vehicular trajectory data is available and show a promising foundation to improve the observability of dynamic traffic demand pattern. However, due to technical and privacy issues, the raw trajectories are not always complete and the paths and destinations between discontinuous trajectory nodes are usually missing. This paper proposes a probabilistic method to jointly reconstruct the missing driving path and destination location of vehicles with incomplete trajectory data. One problem-specific HMM-structured model incorporating spatial and temporal analysis (ST-HMM) is constructed to define the matching probability between observed data and possible movement. Two algorithms, namely candidate set generation and best-match search algorithms, are developed to seek the most possible one as matching result. It can implement end-to-end processing from incomplete trajectory data to complete and connective paths and destinations for the target vehicle. The proposed method is tested based on field-test data and city-wide road network. Compared with two benchmark methods, the proposed method improved the matching accuracy in terms of both path identification and destination inference. Additionally, sensitivity analyses on the size of training dataset and candidate set were performed. We believe that experiment results of these sensitivity analyses can help to provide guidance on data sensing and candidate generation.
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| 5. |
- Li, Yuchen, et al.
(författare)
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Associations of parental and perinatal factors with subsequent risk of stress-related disorders : a nationwide cohort study with sibling comparison
- 2022
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Ingår i: Molecular Psychiatry. - : Springer Nature. - 1359-4184 .- 1476-5578. ; 27, s. 1712-1719
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Tidskriftsartikel (refereegranskat)abstract
- Little is known about the contribution of pregnancy-related parental and perinatal factors to the development of stress-related disorders. We aimed to investigate whether parental/perinatal adversities entail higher risks of stress-related disorders in the offspring, later in life, by accounting for genetic and early environmental factors. Based on the nationwide Swedish registers, we conducted a population-based cohort study of 3,435,747 singleton births (of which 2,554,235 were full siblings), born 1973-2008 and survived through the age of 5 years. Using both population- and sibling designs, we employed Cox regression to assess the association between parental and perinatal factors with subsequent risk of stress-related disorders. We identified 55,511 individuals diagnosed with stress-related disorders in the population analysis and 37,433 in the sibling analysis. In the population-based analysis we observed increased risks of stress-related disorders among offspring of maternal/paternal age <25, single mothers, parity >= 4, mothers with BMI >= 25 or maternal smoking in early pregnancy, gestational diabetes, and offspring born moderately preterm (GA 32-36 weeks), or small-for-gestational-age. These associations were significantly attenuated toward null in the sibling analysis. Cesarean-section was weakly associated with offspring stress-related disorders in population [hazard ratio (HR) 1.09, 95% confidence interval (CI) 1.06-1.12] and sibling analyses (HR 1.10, 95% CI 1.02-1.20). Our findings suggest that most of the observed associations between parental and perinatal factors and risk of stress-related disorders in the population analysis are driven by shared familial environment or genetics, and underscore the importance of family designs in epidemiological studies on the etiology of psychiatric disorders.
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| 6. |
- Li, Yuchen, et al.
(författare)
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Psychological distress among health professional students during the COVID-19 outbreak
- 2021
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Ingår i: Psychological Medicine. - : Cambridge University Press. - 0033-2917 .- 1469-8978. ; 51:11, s. 1952-1954
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Tidskriftsartikel (refereegranskat)abstract
- Background: Due to the drastic surge of COVID-19 patients, many countries are considering or already graduating health professional students early to aid professional resources. We aimed to assess outbreak-related psychological distress and symptoms of acute stress reaction (ASR) in health professional students and to characterize individuals with potential need for interventions.Methods: We conducted a prospective cohort study of 1442 health professional students at Sichuan University, China. At baseline (October 2019), participants were assessed for childhood adversity, stressful life events, internet addiction, and family functioning. Using multivariable logistic regression, we examined associations of the above exposures with subsequent psychological distress and ASR in response to the outbreak.Results: Three hundred and eighty-four (26.63%) participants demonstrated clinically significant psychological distress, while 160 (11.10%) met the criterion for a probable ASR. Individuals who scored high on both childhood adversity and stressful life event experiences during the past year were at increased risks of both distress (ORs 2.00-2.66) and probable ASR (ORs 2.23-3.10), respectively. Moreover, internet addiction was associated with elevated risks of distress (OR 2.05, 95% CI 1.60-2.64) and probable ASR (OR 2.15, 95% CI 1.50-3.10). By contrast, good family functioning was associated with decreased risks of distress (OR 0.43, 95% CI 0.33-0.55) and probable ASR (OR 0.48, 95% CI 0.33-0.69). All associations were independent of baseline psychological distress.Conclusions: Our findings suggest that COVID-19 related psychological distress and high symptoms burden of ASR are common among health professional students. Extended family and professional support should be considered for vulnerable individuals during these unprecedented times.
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| 7. |
- Li, Yuchen, et al.
(författare)
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Public awareness, emotional reactions and human mobility in response to the COVID-19 outbreak in China : a population-based ecological study
- 2022
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Ingår i: Psychological Medicine. - : Cambridge University Press. - 0033-2917 .- 1469-8978. ; 52:9, s. 1793-1800
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Tidskriftsartikel (refereegranskat)abstract
- Background: The outbreak of COVID-19 generated severe emotional reactions, and restricted mobility was a crucial measure to reduce the spread of the virus. This study describes the changes in public emotional reactions and mobility patterns in the Chinese population during the COVID-19 outbreak.Methods: We collected data on public emotional reactions in response to the outbreak through Weibo, the Chinese Twitter, between January 1st and March 31st, 2020. Using anonymized location-tracking information, we analyzed the daily mobility patterns of approximately 90% of Sichuan residents.Results: There were three distinct phases of the emotional and behavioral reactions to the COVID-19 outbreak. The alarm phase (January 19th –26th) was a restriction-free period, characterized by few new daily cases, but enormous public negative emotions (the number of negative comments per Weibo post increased by 246.9 per day, 95%CI: 122.5–371.3), and a substantial increase in self-limiting mobility (from 45.6% to 54.5%, changing by 1.5% per day, 95%CI: 0.7%–2.3%). The epidemic phase (January 27th –February 15th) exhibited rapidly increasing numbers of new daily cases, decreasing expression of negative emotions (a decrease of 27.3 negative comments per post per day, 95%CI: −40.4–−14.2), and a stabilized level of self-limiting mobility. The relief phase (February 16th –March 31st) had a steady decline in new daily cases and decreasing levels of negative emotion and self-limiting mobility.Conclusions: During the COVID-19 outbreak in China, the public’s emotional reaction was strongest before the actual peak of the outbreak and declined thereafter. The change in human mobility patterns occurred before the implementation of restriction orders, suggesting a possible link between emotion and behavior.
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| 8. |
- Ling, Yancheng, et al.
(författare)
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Analyzing factors contributing to bus driver deceleration behavior at intersections using multi-source naturalistic driving data
- 2024
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Konferensbidrag (refereegranskat)abstract
- Understanding the impact of various factors on bus deceleration behavior atintersections has important implications for bus operations control, management, andsafety. This paper introduces a multiple linear regression model to analyze the factorsinfluencing bus driver deceleration (a proxy of safe driving state) at intersections usingdata from multiple sources, including the on-board closed-circuit television (CCTV), the advanced driver assistance system (ADAS), the bus controller area network (CAN), the bus operation data, and the driver profile data. We develop a comprehensive modeldata extraction framework and corresponding methods to effectively estimate/calculatethe bus deceleration rate (dependent variable) and its influencing factors (independent variables). We explored the factors impact on bus deceleration behavior at intersections using data from a typical bus route in China. The results highlight significant factors, including driver characteristics (age), en-route and intersection approaching driving states (trip delay, turnaround time, driving direction, andapproaching speed), traffic states (surrounding vehicles), and intersection characteristics (types, number of lanes, zebra crossing, divider, bus lane, right turnlane, location). Generally, drivers with younger ages (short reaction time) and driving with psychological anticipation of complex situations(surrounding vehicles and pedestrians, unsignalized intersections) tend to decelerate more smoothly. The agencies may enhance safe bus driving behavior by allowing enough turnaround time in timetabling, recommending intersection approaching speed, and providing tailored ADAS system (rather than flooding all alerts). Also, the planning of bus infrastructures(e.g., dedicated lanes and stop locations) should be properly evaluated considering their soft contribution to safe driving behaviors.
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| 9. |
- Liu, Dongjie, et al.
(författare)
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Enhancing choice-set generation and route choice modeling with data- and knowledge-driven approach
- 2024
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Ingår i: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 162
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Tidskriftsartikel (refereegranskat)abstract
- Two central and interconnected problems arise in the specification of a ‘‘complete’’ path-based route choice model: choice-set generation and choice from a choice set. Choice-set generation poses a significant challenge in personalization and the enumeration of the full choice set with large size. Despite the continued prevalence of classic econometric models for modeling choices within a given set, this requirement of knowledge-driven modeling necessitates explicit model structures and intricate domain knowledge, which may result in practical biases. In this study, a Conditional Variational AutoEncoder (CVAE)-based choice set generation model is developed, which approximates the probability distribution of the underlying choice set generation process conditional on individual and OD characteristics without relying on expert knowledge. In order to facilitate a friendly integration between knowledge-driven econometric and machine learning approaches, a neural-embedded route choice model (IAP-NERCM) with implicit availability/perception (IAP) of choice alternatives is proposed to automatically capture the heterogeneity of taste parameters without assuming any a priori relationship. Results based on synthetic data show that the proposed models are capable of reproducing the pre-defined coefficients. Field data of GPS data collected in Toyota City is used to future test the proposed models compared to classical statistical models. Results indicate that IAP-NERCM exhibits the ability to recover underlying taste function and achieves the best performance in terms of goodness-of-fit, predictability, and estimation time.
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| 10. |
- Song, Yuchen, et al.
(författare)
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A state-based inverse reinforcement learning approach to model activity-travel choices behavior with reward function recovery
- 2024
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Ingår i: Transportation Research Part C. - : Elsevier BV. - 0968-090X .- 1879-2359. ; 158
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Tidskriftsartikel (refereegranskat)abstract
- Behaviorally oriented activity-travel choices (ATC) modeling is a principal part of travel demand analysis. Traditional econometric and rule-based methods require explicit model structures and complex domain knowledge. While several recent studies used machine learning models, especially adversarial inverse reinforcement learning (IRL) models, to learn potential ATC patterns with less expert-designed settings, they lack a clear representation of rational ATC behavior. In this study, we propose a data-driven IRL framework based on the maximum causal approach to minimize f-divergences between expert and agent state marginal distributions, which provides a more sample-efficient measurement. In addition, we specify a separate state-only reward function and derive an analytical gradient of the f-divergence objective with respect to reward parameters to ensure good convergences. The method can recover a stationary reward function, which assures the agent to get close to the expert behavior when training from scratch. We validate the proposed model using cellular signaling data from Chongqing, China by comparing with baseline models (behavior cloning, policy-based, and reward-based models) in aspects of policy performance comparison, reward recovery, and reward transfer tasks. The experiment results indicate that the proposed model outperforms existing methods and is relatively less sensitive to the number of expert demonstrations. Qualitative analyses are provided on the fundamental ATC preferences on different features given the reward function recovered from the observed mobility trajectories, and on the learning behaviors under different choices of f-divergence.
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| 11. |
- Xu, Leilei, et al.
(författare)
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A skeletal chemical kinetic mechanism for ammonia/n-heptane combustion
- 2023
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Ingår i: Fuel. - : Elsevier BV. - 0016-2361. ; 331
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Tidskriftsartikel (refereegranskat)abstract
- Progressively stricter pollutant emission targets in international agreements have shifted the focus of combustion research to low carbon fuels. Ammonia is recognized as one of the promising energy vectors for next-generation power production. Due to the low flame speed and high auto-ignition temperature, ammonia is often burned with a high reactivity pilot fuel (e.g. diesel). However, chemical kinetic mechanisms describing the combustion of ammonia and large hydrocarbon fuels (such as n-heptane, a surrogate of diesel) are less developed. In this work, a skeletal chemical kinetic mechanism for n-heptane/ammonia blend fuels is proposed using a joint decoupling methodology and optimization algorithm. A sensitivity analysis of the ignition delay times of the ammonia/n-heptane mixture is performed to identify the dominant reactions. A genetic algorithm is used to optimize the mechanism further. The final skeletal mechanism is made up of 69 species and 389 reactions. The skeletal ammonia/n-heptane mechanism is validated against the experimental data for combustion of pure ammonia, ammonia/hydrogen and ammonia/n-heptane mixtures, including the global combustion characteristic parameters such as ignition delay times measured in shock tubes or rapid compression machines, laminar burning velocities measured in heat flux burners or spherical flame vessels, and species data measured in jet-stirred reactors. Comparing the results from the skeletal mechanism with those from other mechanisms from the literature is conducted to evaluate the mechanism further. The present skeletal mechanism can well predict the combustion processes for a wide range of conditions, and the mechanism is computationally efficient, showing good potential to model ammonia/n-heptane combustion with good accuracy and efficiency.
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