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BackgroundFor the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. ObjectiveThis study aimed to develop and test the feasibility of a “patients-like-me” framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. MethodsOur framework used COVID-19–like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19–like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19–like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. ResultsCompared to the COVID-19–like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19–like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. ConclusionsWe provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

The purpose of this study is to examine the current state of job crafting and career commitment of rural primary teachers and to analyze the relationship between the two variables as well as the influencing mechanisms through a questionnaire survey of 2,217 primary school teachers in Liaoning, Yunnan, Chongqing, Guangxi, and Guizhou. The research findings showed that rural primary teachers exhibited a moderate level of capacity for job crafting and career commitment; that job crafting positively predicted career commitment among rural primary teachers; and that teachers' self-efficacy and organizational support played mediating and moderating roles respectively in the relationship between the two variables

AIM:To compare the corneal refractive power and visual quality after three kinds of corneal refractive surgery: laser subepithelial keratomileusis(LASEK), transepithelial photorefractive keratectomy(TransPRK)and femtosecond laser assisted in situ keratomileusis(FS-LASIK).METHODS: Retrospective study. A total of 232 patients(464 eyes)who underwent elective corneal refractive surgery in our hospital from January 2021 to December 2022 were selected as the study subjects. They were divided into the LASEK group of 70 cases(140 eyes), TransPRK group of 76 cases(152 eyes), and FS-LASIK group of 86 cases(172 eyes)according to the different surgical choices made by the patients. Uncorrected visual acuity(UCVA), visual quality, biomechanical stability, tear film break-up time(BUT), Schirmer Ⅰ test(SⅠt)and quality of life were compared among the three groups at 1 and 3 mo postoperatively.RESULTS: The UCVA of the three groups significantly improved at 1 and 3 mo postoperatively, the total high-order aberrations, spherical aberration, horizontal coma, and vertical coma of the entire cornea were significantly increased, while the biomechanical stability in all three groups were significantly reduced(all P0.05).CONCLUSION:In the treatment of ametropia, LASEK, TransPRK and FS-LASIK all have certain effects, and have similar results in improving UCVA and increasing visual quality. However, TransPRK and LASEK have more stable biomechanical properties than FS-LASIK, and have better efficacy in improving patients' quality of life.

Based on the baseline data of the China Education Panel Survey, this paper explored the relationship between teacher training and academic performance in urban and rural samples respectively and the impact of teacher training on the urban-rural gap of students' academic performance. The results showed that: firstly, there was a significant urban-rural gap in academic performance, and the gap in high quantiles and language subjects were even larger. Secondly, the results of unconditional quantile regression showed that teacher training could improve the performance of urban students with different academic levels and rural students with intermediate or above academic levels, but it cannot improve the performance of rural students with lower academic levels. In addition, the overall effect of teacher training in urban areas is significantly higher than that in rural areas. Thirdly, different quantiles of Oaxaca-Blinder decomposition found that the endowment effect and the coefficient effect of teacher training were the important causes of the urban-rural performance gap, but the relative sizes of the two were different according to the different grades and different quantiles of performance distribution. Therefore, to increase the training opportunities and improve the training quality of rural teachers as well as enhance the resource conversion rate of rural students are of great practical significance for narrowing the urban-rural performance gap.

California sits at the epicenter of self-driving vehicle technology development, with numerous companies testing connected and automated vehicles (CAVs) in the state. CAVs have the potential to improve safety and increase mobility for children, the elderly, and people with disabilities. These vehicles will operate more efficiently, use less space on the roadway, and cause fewer crashes, all of which are expected to relieve traffic congestion. However, CAVs will also likely bring about complex changes to travel demand, urban design, and land use. The degree to which these changes will affect vehicle miles traveled, energy use, and air pollution in California is unknown and could have wideranging implications for the state’s ability to meet its climate goals.Researchers at the University of California, Davis investigated the range of potential impacts that rapid adoption of CAVs in California might have on vehicle miles traveled and emissions. The researchers estimated the vehicle miles traveled and emissions of each scenario using a statewide travel demand model, emissions factors from California agencies, and assumptions derived from the scientific literature and expert input. This policy brief summarizes the findings from that research and provides policy implications.View the NCST Project Webpage

This project aims to provide recommendations on the methodology and design specifications for the travel demand model to be built for the Link21 program in the Northern California megaregion. The Link21 program is a major rail investment program that will considerably improve and upgrade the passenger rail services in the Northern California megaregion, centered around the Transbay Corridor between Oakland and San Francisco in the San Francisco Bay Area. To support this effort, we reviewed the current and potential travel markets for the Link21 program, assessed the available travel demand models that could be used to support the modeling efforts for the Link21 program, and conducted interviews with experts from academic institutions, metropolitan planning organizations, state and federal agencies, and US DOE national labs. Considering the goals and objectives of the Link21 program, a list of 20 critical, important, and optional modeling features were identified, which should be considered for the Link21 program. We reviewed 11 existing travel demand models based on the evaluation of their modeling features, and present four proposed modeling approaches which could be considered to support the Link21 program. For each modeling approach, we summarize pros and cons in terms of fulfilling the requirements of the Link21 program. The four modeling approaches include: 1) building on the Metropolitan Transportation Commission (MTC) TM 2.1 regional travel demand model without a dedicated long-distance travel model component; 2) building on the MTC TM 2.1 regional travel demand model with a dedicated long-distance travel model component; 3) building on the San Francisco County Transportation Authority (SFCTA) regional travel demand model with or without a dedicated long-distance travel model component; and 4) building on the California High Speed Rail (CHSR) or the new statewide rail model that is currently under development. The study also discusses some sources of uncertainties that might affect future travel demand and the modeling practice in the Link21 regions. These include the impacts of the COVID-19 pandemic on work patterns and activity/travel choices, the introduction of shared mobility services, micromobility, the potential deployment of Mobility as a Service (MaaS) solutions, and the forthcoming deployment of connected and automated vehicles (CAVs). Given the complexity of the Link21 program and the requested 18-month timeline for developing a new travel demand model to support the program, we recommend that the model development for the Link21 program build on an existing modeling framework and adopt a modular system, which can be updated over time. An initial model release would become available in the proposed timeline of 18 months, while future updates and improvements in the model components could be added in future model updates. This process also would be well-suited to address eventual modeling issues that could arise with the initial model release, and it would benefit from the development and updates of other models in the Northern California megaregion that are being carried out in parallel.

Abstract Head and neck squamous cell carcinoma (HNSCC) is a common tumour type in otorhinolaryngology, and its occurrence is related to long-term exposure to tobacco and alcohol. Recently, HPV infection has become an increasingly important contributor to HNSCC, and HPV-associated HNSCC has a different clinical course and better prognosis than non-HPV-associated HNSCC. However, the exact molecular mechanism of HNSCC is unclear. Here, we obtained data from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) to analyse the mitophagy process and related influencing factors of HPV-associated HNSCC via the integration of bioinformatics analysis and experimental validation. We found that in HPV-associated HNSCC, process of mitophagy affects tumour development, immune cell infiltration and prognosis. In the mitophagy process of HPV-related HNSCC: NOS2, IL17REL, TMSB15A, TUBB4A and other hub genes showed significantly higher expression levels than in non-HPV-related HNSCC. Furthermore, this was also confirmed by quantitative real-time PCR (qRT‒PCR), which was used to detect the expression of differentially expressed genes in HNSCC tissues. Furthermore, we found that the unique immunological characteristics by expressing CD8+ T cell in a high level in HPV-related HNSCC, and the scores obtained from the score model affected the prognosis of patients. In conclusion, our study revealed the unique biomolecular signature of mitophagy in HPV-associated HNSCC, which may contribute to the development of precise treatment regimens for HPV-associated HNSCC patients.

This study helps understand how the anticipated emergence of autonomous vehicles will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. The study begins with a literature review on connected and automated vehicle (CAV) technology for light-duty vehicles, the factors likely to affect CAV adoption, expected impacts of CAVs, and approaches to modeling these impacts. The study then uses a set of modifications in the California Statewide Travel Demand Model (CSTDM) to simulate the following scenarios for the deployment of passenger light-duty CAVs in California by 2050: (0) Baseline (no automation); (1) Private CAV; (2) Private CAV + Pricing; (3) Private CAV + Zero emission vehicles (ZEV); (4) Shared CAV; (5) Shared CAV + Pricing; (6) Shared CAV + ZEV. The modified CSTDM is used to forecast travel demand and mode share for each scenario, and this output is used in combination with the emission factors from the EMission FACtor model (EMFAC) and Vision model to calculate energy consumption and criteria pollutant emissions. The modeling results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled and emissions will likely increase, due to the relative convenience of CAVs. The study also reveals limitations in models like the CSTDM that primarily use sociodemographic factors and job/residence location as inputs for the simulation of activity participation and tour patterns, without accounting for some of the disruptive effects of CAVs. The study results also show that total vehicle miles traveled and vehicle hours traveled could be substantially impacted by a modification in future auto travel costs. This means that the eventual implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help curb tailpipe pollutant emissions in future scenarios, though they may not be able to completely offset the increases in travel demand and road congestion that might result from CAV deployment. Such policies should be considered to counteract and mitigate some of the undesirable impacts of CAVs on society and on the environment.