Forward Vehicle Collision Warning Based on Quick Camera Calibration

Forward Vehicle Collision Warning (FCW) is one of the most important functions for autonomous vehicles. In this procedure, vehicle detection and distance measurement are core components, requiring accurate localization and estimation. In this paper, we propose a simple but efficient forward vehicle collision warning framework by aggregating monocular distance measurement and precise vehicle detection. In order to obtain forward vehicle distance, a quick camera calibration method which only needs three physical points to calibrate related camera parameters is utilized. As for the forward vehicle detection, a multi-scale detection algorithm that regards the result of calibration as distance priori is proposed to improve the precision. Intensive experiments are conducted in our established real scene dataset and the results have demonstrated the effectiveness of the proposed framework

Study on multivariate regression model of indoor and outdoor particulate pollution in severe cold area of China

At present, the widespread existence of haze phenomenon has a serious impact on indoor air quality. Indoor particulate pollution has been paid more and more attention by the society. However, the correlation and diffusion mechanism of indoor and outdoor particulate matter are still controversial. In order to explore the correlation between indoor and outdoor particulate matter of different building types in heating season and non-heating season, the indoor and outdoor particulate concentrations and meteorological parameters of 110 stations in severe cold area of China were monitored by experiments. The analysis shows that indoor and outdoor temperature, humidity, air velocity, wind direction and atmospheric pressure are the main factors affecting indoor and outdoor particulate concentration. And based on these factors, it can model the indoor predicted particulate concentrations by multivariate regression. It also shows a significant difference in the relationship between the concentration of particulate matter and factors of indoor and outdoor particulate matter. Therefore, this study provides a good premise for exploring the health risks and control measures of particulate matter

TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models

Data augmentation has been established as an efficacious approach to supplement useful information for low-resource datasets. Traditional augmentation techniques such as noise injection and image transformations have been widely used. In addition, generative data augmentation (GDA) has been shown to produce more diverse and flexible data. While generative adversarial networks (GANs) have been frequently used for GDA, they lack diversity and controllability compared to text-to-image diffusion models. In this paper, we propose TTIDA (Text-to-Text-to-Image Data Augmentation) to leverage the capabilities of large-scale pre-trained Text-to-Text (T2T) and Text-to-Image (T2I) generative models for data augmentation. By conditioning the T2I model on detailed descriptions produced by T2T models, we are able to generate photo-realistic labeled images in a flexible and controllable manner. Experiments on in-domain classification, cross-domain classification, and image captioning tasks show consistent improvements over other data augmentation baselines. Analytical studies in varied settings, including few-shot, long-tail, and adversarial, further reinforce the effectiveness of TTIDA in enhancing performance and increasing robustness

Genetic Analysis of KRT9 Gene Revealed Previously Known Mutations and Genotype-Phenotype Correlations in Epidermolytic Palmoplantar Keratoderma

Epidermolytic palmoplantar keratoderma (EPPK, OMIM 144200) is an autosomal dominant inherited disease, clinically characterized by diffuse yellowish thickening of the skin on the palms and soles, usually with erythematous borders developing during the first weeks or months after birth. Pathogenesis of EPPK is determined by mutations in the keratin gene (KRT9). Thirty three mutations in the KRT9 gene from 100 EPPK families have been identified. Among these, 23 mutations are located in the 1A region (a mutation hot spot region), 7 are located in the 2B region, and the remaining 3 are synonymous mutations. In this study, three heterozygous mutations (p.N161S, p.R163W, and p.R163Q), located in regions of the gene encoding the conserved central a-helix rod domain, were detected in the KRT9 gene of the three large Chinese families. This study confirms that codon 163 (48 of 100 cases) is a hot spot mutation site for KRT9. Additional findings identified p.N161S (4%) and p.R163W (4%) as potential hot spot mutations for EPPK associated with knuckle pads, and p.R163Q (15 of 100 cases) as the hot spot mutation of EPPK not occurring in combination with knuckle pads. In conjunction with future studies, this research may help lay the foundation for genetics counseling, prenatal diagnosis and clinical treatment of EPPK

Mitochondrial DNA heteroplasmy analysis in keratoconus patients from China

Background: Mitochondrial DNA (mtDNA) variants have been implicated in keratoconus (KC). The present study aimed to characterize the mtDNA heteroplasmy profile in KC and explore the association of mitochondrial heteroplasmic levels with KC.Methods: Mitochondrial sequencing of peripheral blood samples and corneal tomography were conducted in 300 KC cases and 300 matched controls. The number of heteroplasmic and homoplasmic variants was calculated across the mitochondrial genome. Spearman’s correlation was used to analyze the correlation between the number of heteroplasmic variants and age. The association of mtDNA heteroplasmic level with KC was analyzed by logistic regression analysis. Moreover, the relationship between mitochondrial heteroplasmic levels and clinical parameters was determined by linear regression analysis.Results: The distribution of mtDNA heteroplasmic variants showed the highest number of heteroplasmic variants in the non-coding region, while the COX3 gene exhibited the highest number in protein-coding genes. Comparisons of the number of heteroplasmic and homoplasmic non-synonymous variants in protein-coding genes revealed no significant differences between KC cases and controls (all p > 0.05). In addition, the number of heteroplasmic variants was positively associated with age in all subjects (r = 0.085, p = 0.037). The logistic regression analyses indicated that the heteroplasmic levels of m.16180_16181delAA was associated with KC (p < 0.005). Linear regression analyses demonstrated that the heteroplasmic levels of m.16180_16181delAA and m.302A>C were not correlated with thinnest corneal thickness (TCT), steep keratometry (Ks), and flat keratometry (Kf) (all p > 0.05) in KC cases and controls separately.Conclusion: The current study characterized the mtDNA heteroplasmy profile in KC, and revealed that the heteroplasmic levels of m.16180_16181delAA were associated with KC

Accuracy of tomographic and biomechanical parameters in detecting unilateral post-LASIK keratoectasia and fellow eyes

Background: Patients with unilateral post-LASIK keratectasia (KE) have clinical ectasia in one eye but not in the fellow eye. As serious complications, these cases are rarely reported but are worth investigating. This study aimed to explore the characteristics of unilateral KE and the accuracy of corneal tomographic and biomechanical parameters to detect KE and distinguish fellow eyes from control eyes.Methods: The study analyzed 23 KE eyes, 23 KE fellow eyes, and 48 normal eyes from age- and sex-matched patients who had undergone LASIK. The Kruskal–Wallis test and further paired comparisons were performed to compare the clinical measurements of the three groups. The receiver operating characteristic curve was used to evaluate the ability to distinguish KE and fellow eyes from the control eyes. Binary logistic regression with the forward stepwise method was performed to produce a combined index, and the DeLong test was used to compare the discriminability difference of the parameters.Results: Males accounted for 69.6% of patients with unilateral KE. The duration between corneal surgery and the onset of ectasia ranged from 4 months to 18 years, with a median time of 10 years. The KE fellow eye had a higher posterior evaluation (PE) value than the control eyes (5 vs. 2, p = 0.035). Diagnostic tests showed that PE, posterior radius of curvature (3 mm), anterior evaluation (FE), and Corvis biomechanical index–laser vision correction (CBI-LVC) were sensitive indicators for distinguishing KE in the control eyes. The ability of PE to detect the KE fellow eye from the control eye was 0.745 (0.628 and 0.841), with 73.91% sensitivity and 68.75% specificity at a cut-off value of 3. The ability of a combined index, constructed using PE and FE, to distinguish fellow eyes of KE from controls was 0.831 (0.723 and 0.909), which was higher than that of PE and FE individually (p < 0.05).Conclusion: The fellow eyes of patients with unilateral KE had significantly higher PE values than control eyes, and a combination of PE and FE enhanced this differentiation in a Chinese population. More attention should be paid to the long-term follow-up of patients after LASIK and to be wary of the occurrence of early KE

Machine learning analysis with the comprehensive index of corneal tomographic and biomechanical parameters in detecting pediatric subclinical keratoconus

Background: Keratoconus (KC) occurs at puberty but diagnosis is focused on adults. The early diagnosis of pediatric KC can prevent its progression and improve the quality of life of patients. This study aimed to evaluate the ability of corneal tomographic and biomechanical variables through machine learning analysis to detect subclinical keratoconus (SKC) in a pediatric population.Methods: Fifty-two KC, 52 SKC, and 52 control pediatric eyes matched by age and gender were recruited in a case-control study. The corneal tomographic and biomechanical parameters were measured by professionals. A linear mixed-effects test was used to compare the differences among the three groups and a least significant difference analysis was used to conduct pairwise comparisons. The receiver operating characteristic (ROC) curve and the Delong test were used to evaluate diagnostic ability. Variables were used in a multivariate logistic regression in the machine learning analysis, using a stepwise variable selection to decrease overfitting, and comprehensive indices for detecting pediatric SKC eyes were produced in each step.Results: PE, BAD-D, and TBI had the highest area under the curve (AUC) values in identifying pediatric KC eyes, and the corresponding cutoff values were 12 μm, 2.48, and 0.6, respectively. For discriminating SKC eyes, the highest AUC (95% CI) was found in SP A1 with a value of 0.84 (0.765, 0.915), and BAD-D was the best parameter among the corneal tomographic parameters with an AUC (95% CI) value of 0.817 (0.729, 0.886). Three models were generated in the machine learning analysis, and Model 3 (y = 0.400*PE + 1.982* DA ratio max [2 mm]−0.072 * SP A1−3.245) had the highest AUC (95% CI) value, with 90.4% sensitivity and 76.9% specificity, and the cutoff value providing the best Youden index was 0.19.Conclusion: The criteria of parameters for diagnosing pediatric KC and SKC eyes were inconsistent with the adult population. Combined corneal tomographic and biomechanical parameters could enhance the early diagnosis of young patients and improve the inadequate representation of pediatric KC research

A capital-based approach to better understand health inequalities: Theoretical and empirical explorations

Background: The persistence of health inequalities may be driven by differences in education and income, but also by other economic and non-economic factors. Our aim was to explore how the association between single-dimensional health and socioeconomic status (SES) changes when including health-related person capital, economic capital, social capital, cultural capital and attractiveness and personality capital. Methods: We used a capital-based approach to understand health inequalities. It presumes intertwined relationships between broadly measured health (‘health-related person capital’) and embodied resources (‘attractiveness and personality capital’) on the one hand, and ESC capital, i.e., economic, social, and cultural resources on the other. We used cross-sectional data on 152,592 participants from the Dutch Lifelines cohort study and estimated correlations using partial least squares structural equation modelling. Results: The correlation between SES and health-related person capital (r = 0.15) was stronger than the correlations between SES and single-dimensional health (physical and mental health; r = 0.12 and r = 0.04, respectively). ESC capital, combining economic, social and cultural capital, showed a correlation of 0.34 with health-related person capital. This was stronger than the correlation between health-related person capital and economic capital alone (r = 0.19). Lastly, the correlation between health-related person capital and ESC capital increased when health related, attractiveness and personality resources were combined into a single person capital construct (from r = 0.34 to r = 0.49). Conclusions: This exploratory study shows the empirical interconnectedness of various types of resources, and their potential role in the persistence of health inequalities. Our findings corroborate the idea of considering health as a multidimensional concept, and to extend conventional SES indicators to a broader measurement of economic and non-economic resources