Preparing CT imaging datasets for deep learning in lung nodule analysis:Insights from four well-known datasets

Background: Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering.Methods: The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order.Findings: The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided.Discussion and conclusion: Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.</p

Analysis on the factors associated with COVID-19 infection among Chinese residents after the implementation of the 10 new rules to optimize COVID-19 response: a cross-sectional study

IntroductionThis study aimed to investigate the status of COVID-19 infection and the associated factors among Chinese residents after the implementation of the 10 New Rules to optimize COVID response.MethodsParticipants were recruited using convenience sampling. The study used self-filled questionnaires to examine COVID-19 infection and associated factors among Chinese residents, from December 29, 2022, to January 2, 2023. For the statistical analysis, descriptive and quantitative analyses were used. The potential risk factors for COVID-19 infection were identified by multivariable logistic regression analysis.ResultsAfter the adjustments in control strategies against COVID-19, the infection rate of COVID-19 was high among respondents, and 98.4% of individuals who tested positive showed symptoms including cough, fever, fatigue, headache, sore throat, nasal congestion, sputum production, muscle and joint pain, and runny nose. The main problems respondents reported were the shortage of drugs and medical supplies, the increased burden on families, and the unreliable information source of COVID-19 infection. Logistic regression showed that isolating patients with COVID-19 at home was associated with a lower risk of COVID-19 infection (OR = 0.58, 95%CI: 0.42–0.81).ConclusionCOVID-19 infection among residents is closely related to age, gender, and epidemic prevention measures. The government needs to strengthen education for individuals and centrally manage and properly address difficulties that may arise during COVID-19

Thermistor-based airflow sensing on a flapping wing micro air vehicle

Flow sensing exists widely in nature to help animals perform certain tasks. It has also been widely adopted in engineering applications with different types of sensing instrumentation. In particular, in the field of aerospace engineering, airflow sensing is crucial to vehicle state evaluation and flight control. This project surveys the key mechanisms from biological features in nature that enable flow sensing and expands towards the application motivation to identify a suitable airflow sensor that can be equipped to a flapping wing micro air vehicle (FWMAV) for onboard airflow sensing. The selection of sensors is first narrowed down to three major types of airflow sensors from the state of art that have the most potential to be integrated onboard a flapping wing MAV, considering the sensor performance need, size, weight and power (SWaP) restrictions. Two thermal-based commercially available low-cost airflow sensors RevP and RevC from Modern Device have been selected after the trade-off analysis. A full workflow of calibrating and evaluating the two airflow sensors' directional sensitivity has been carried out through two wind tunnel campaigns. Their performance under grid-generated turbulence is compared with a constant temperature hot-wire anemometer. This series of tests leads to the conclusion that the RevP airflow sensor has better performance and is therefore chosen to be placed onboard a flapping wing MAV Delfly Nimble. Both mounted tests and tethered hovering tests with the Delfly Nimble are performed to further examine the airflow sensor RevP's measurement performance under different influence factors such as MAV throttle levels, MAV body pitch angles and freestream speeds. In the end, it is concluded that as a proof of concept, the RevP sensor is capable of performing effective measurements for low flow speeds less than 4 m/s, within the pitching angle range of -30 to 30 degrees. Although this is the first achieved tethered hover flight with onboard airflow sensing for a flapping wing MAV, its limited payload and onboard power supply demands an even smaller and less power consuming design of airflow sensors to enable further applications such as autonomous reactive control under wind disturbances.Aerospace Engineerin

Smart Cup for In-Situ 3D Measurement of Wall-Mounted Debris via 2D Sensing Grid in Production Pipelines

The accumulation of separated out impurities from pipeline transported medium onto the pipe wall is a major cause of downtime maintenance of oil and gas production systems. To regularly scrub off wall-mounted debris and probe the severity, pipeline inspection gauges (PIG) are the state-of-the-art tools developed for the task, using the pressure differential across the device as the driving force, and tag-along sensing equipment for wall defects measurement. Currently, the PIG propulsion and sensing tasks are realized by separate compartments, limited to large diameter operations. In this work, a soft solution for medium to small diameter pipelines has been demonstrated. The smart cup with integrated sensing grid is proposed to achieve integrated wall-mounted debris dimensional measurement, without the need of additional sensors. To achieve the goal, this work starts from the mathematical modelling of the geometric problem, to new fabrication procedures, experimental setup, and finally finishes with validation results. Initial results have shown that using the proposed smart cup, the wall-mounted debris can be detected, with modelling error maxed at 5.1%, and deformation detection accuracy between 1.18% and 1.92% with respect to the outer diameter

Drug Nanocrystals for Active Tumor-Targeted Drug Delivery

Drug nanocrystals, which are comprised of active pharmaceutical ingredients and only a small amount of essential stabilizers, have the ability to improve the solubility, dissolution and bioavailability of poorly water-soluble drugs; in turn, drug nanocrystal technology can be utilized to develop novel formulations of chemotherapeutic drugs. Compared with passive targeting strategy, active tumor-targeted drug delivery, typically enabled by specific targeting ligands or molecules modified onto the surface of nanomedicines, circumvents the weak and heterogeneous enhanced permeability and retention (EPR) effect in human tumors and overcomes the disadvantages of nonspecific drug distribution, high administration dosage and undesired side effects, thereby contributing to improving the efficacy and safety of conventional nanomedicines for chemotherapy. Continuous efforts have been made in the development of active tumor-targeted drug nanocrystals delivery systems in recent years, most of which are encouraging and also enlightening for further investigation and clinical translation

Long-term effect of mobile phone-based education and influencing factors of willingness to receive HPV vaccination among female freshmen in Shanxi Province, China

Background This study was conducted to characterize the long-term effect of mobile-based education on Chinese female freshmen and disclose the possible predictors of their willingness to get vaccinated based on the information-motivation-behavioral skills (IMB) model. Methods We randomly assigned 509 participants to a 7-day mobile-based educational intervention or control group and collected information about general information, health, and sexual behavior, HPV vaccination intention and action, HPV-related knowledge, cognition, and behavioral skill by an online self-administrated questionnaire at baseline, post-intervention, and at the 1-month and 3-month follow-ups. Results The intervention arm showed an improvement in IMB scores after education. Despite the persistent improvement in knowledge, the improvement in their motivation and behavioral skills decreased at the 1-month and 3-month follow-ups. Participants’ vaccination willingness was elevated after the baseline survey in both the intervention and control groups, while the overall appointment/vaccination rate was only 3.73% 3 months later. The intention to get vaccinated was associated with knowing HPV (adjusted OR: 2.37, 95% CI: 1.44 – 3.89), perceiving more barriers (adjusted OR: 2.16, 95% CI: 1.44 – 3.25), higher subjective norms (adjusted OR: 2.05, 95% CI: 1.26 – 3.32), and having more behavioral skills (adjusted OR: 2.95, 95% CI: 1.79 – 4.87). Conclusion Seven-day mobile-based education was effective to increase IMB model scores among female freshmen. However, the improvement in motivation and behavioral skills was not persistent. Information, perceived barriers, subjective norms, and behavioral skills were discovered to be influencing factors of vaccination intention. Future research with longer, more convenient, and more tailored education to the main influencing factors is warranted

Additional file 5: of Long noncoding RNA expression profile and association with SLEDAI score in monocyte-derived dendritic cells from patients with systematic lupus erythematosus

Table S4. The predicted trans-associated genes of the differentially expressed lncRNAs. (XLSX 7838 kb

Additional file 2: of Long noncoding RNA expression profile and association with SLEDAI score in monocyte-derived dendritic cells from patients with systematic lupus erythematosus

Table S2. The specific information about the differentially expressed mRNAs. (XLSX 83 kb

Additional file 6: of Long noncoding RNA expression profile and association with SLEDAI score in monocyte-derived dendritic cells from patients with systematic lupus erythematosus

Figure S2. LncRNAs and targeted genes regulating cytokine and chemokine networks. The node color differs from dark green to dark red according to the connection numbers from small to large. Squares represent target genes. Circles represent lncRNAs. Arrows represent positive correlation and terminated lines represent negative correlation. (PDF 32 kb

Additional file 4: of Long noncoding RNA expression profile and association with SLEDAI score in monocyte-derived dendritic cells from patients with systematic lupus erythematosus

Table S3. The predicted cis-associated genes of the differentially expressed lncRNAs. (XLSX 2714 kb