Improved ambient air quality is associated with decreased prevalence of childhood asthma and infancy shortly after weaning is a sensitive exposure window.

BACKGROUND: The urban ambient air quality has been largely improved in the past decade. It is unknown whether childhood asthma prevalence is still increasing in ever top-ranking city of Shanghai, whether the improved air quality is beneficial for children's asthma and what time window of exposure plays critical roles. METHODS: Using a repeat cross-sectional design, we analyzed the association between early life exposure to particles and wheezing/asthma in each individual and combined surveys in 2011 and 2019, respectively, in 11,825 preschool children in Shanghai. RESULTS: A significantly lower prevalence of doctor-diagnosed asthma (DDA) (6.6% vs. 10.5%, p < 0.001) and wheezing (10.5% vs. 23.2%, p < 0.001) was observed in 2019 compared to 2011. Exposure to fine particulate matter (PM2.5 ), coarse particles (PM2.5-10 ) and inhalable particles (PM10 ) was decreased in 2019 by 6.3%, 35.4%, and 44.7% in uterus and 24.3%, 20.2%, and 31.8% in infancy, respectively. Multilevel log-binomial regression analysis showed exposure in infancy had independent association with wheezing/DDA adjusting for exposure in uterus. For each interquartile range (IQR) increase of infancy PM2.5 , PM2.5-10 and PM10 exposure, the odds ratios were 1.39 (95% confidence interval (CI): 1.24-1.56), 1.51 (95% CI:1.15-1.98) and 1.53 (95% CI:1.27-1.85) for DDA, respectively. The distributed lag non-linear model showed the sensitive exposure window (SEW) was 5.5-11 months after birth. Stratified analysis showed the SEWs were at or shortly after weaning, but only in those with <6 months of exclusive breastfeeding. CONCLUSIONS: Improved ambient PM benefits in decreasing childhood asthma prevalence. We firstly reported the finding of SEW to PM at or closely after weaning on childhood asthma.

Evolution of the oxidation behaviors of highly oxidation-resistant (Ti0.8Nb0.2)C in 1000-1200 °C steam.

The evolution of the oxidation behaviors of the highly oxidation-resistant (Ti0.8Nb0.2)C was investigated in a 1000-1200 °C steam environment. For the specimen oxidized below 1200 °C, a compact oxide layer less than 7 µm thick was obtained. By increasing the temperature to 1200 °C, the oxide layer grows quickly to over 30 µm. Variable distribution of Ti and Nb in the oxide scales reveals the significance of the outward diffusion of the metal elements during oxidation. Synchronized variations in Ti and Nb were observed in the specimen oxidized below 1200 °C, while the distribution trends of Ti and Nb were opposite in the specimen oxidized at 1200 °C. The incorporation of Nb effectively lowered the diffusion rate of Ti through the oxide scales and the grain growth of the oxides. Suppressed diffusion and dense oxide scales are responsible for the excellent oxidation resistance of (Ti0.8Nb0.2)C below 1200 °C.

A Highly Efficient Model to Study the Semantics of Salient Object Detection.

CNN-based salient object detection (SOD) methods achieve impressive performance. However, the way semantic information is encoded in them and whether they are category-agnostic is less explored. One major obstacle in studying these questions is the fact that SOD models are built on top of the ImageNet pre-trained backbones which may cause information leakage and feature redundancy. To remedy this, here we first propose an extremely light-weight holistic model tied to the SOD task that can be freed from classification backbones and trained from scratch, and then employ it to study the semantics of SOD models. With the holistic network and representation redundancy reduction by a novel dynamic weight decay scheme, our model has only 100K parameters,  âˆ¼  0.2% of parameters of large models, and performs on par with SOTA on popular SOD benchmarks. Using CSNet, we find that a) SOD and classification methods use different mechanisms, b) SOD models are category insensitive, c) ImageNet pre-training is not necessary for SOD training, and d) SOD models require far fewer parameters than the classification models. The source code is publicly available at https://mmcheng.net/sod100k/.

Cost-effectiveness of olaparib maintenance therapy when used with and without restriction by BRCA1/2 mutation status for platinum-sensitive relapsed ovarian cancer.

Objectives: To determine whether olaparib maintenance therapy, used with and without restriction by BRCA1/2 mutation status, is cost-effective at the population level for platinum-sensitive relapsed ovarian cancer in Singapore.Methods: A partitioned survival model compared three management strategies: 1) treat all patients with olaparib; 2) test for germline BRCA1/2 mutation, followed by targeted olaparib use in mutation carriers only; 3) observe all patients. Mature overall survival (OS) data from Study 19 and a 15-year time horizon were used and direct medical costs were applied. Sensitivity analyses were conducted to explore uncertainties.Results: Treating all patients with olaparib was the most costly and effective strategy, followed by targeted olaparib use, and observation of all patients. Base-case incremental cost-effectiveness ratios (ICERs) for all-olaparib and targeted use strategies were SGD133,394 (USD100,926) and SGD115,736 (USD87,566) per quality-adjusted life year (QALY) gained, respectively, compared to observation. ICERs were most sensitive to the cost of olaparib, time horizon and discount rate for outcomes. When these parameters were varied, ICERs remained above SGD92,000 (USD69,607)/QALY.Conclusions: At the current price, olaparib is not cost-effective when used with or without restriction by BRCA1/2 mutation status in Singapore, despite taking into account potential OS improvement over a long time horizon.

An Asian Perspective on Gender Differences in Clinical Outcomes and Echocardiographic Profiles of Patients With Medically Managed Severe Aortic Stenosis.

BACKGROUND: Gender differences in valvular heart disease are increasingly recognised. A prior study has suggested better surgical outcomes in women with symptomatic aortic stenosis (AS). We investigate gender differences in medically managed severe AS. METHOD: We studied 347 patients with severe AS (aortic valve area index <0.6 cm2/m2) in terms of baseline clinical background, echocardiographic characteristics, and clinical outcomes. Appropriate univariate and multivariate models were employed, while Kaplan-Meier curves were constructed to compare mortality outcomes. RESULTS: In total, 205 (59%) patients were women. Despite higher incidences of hypertension (75.6% vs 47.3%) and diabetes mellitus (46.5% vs 29.5%) in women, women had improved survival (Kaplan-Meier log-rank = 6.24, p = 0.012). After adjusting for age (hazard ratio [HR], 1.034; 95% confidence interval [CI], 1.014-1.054), hypertension (HR, 1.469; 95% CI, 0.807-2.673), diabetes (HR, 1.219; 95% CI, 0.693-2.145), and indexed aortic valve area (HR 0.145, 95% CI 0.031-0.684) on multivariate analyses, female gender remained independently associated with lower mortality (HR, 0.561; 95%, CI 0.332-0.947). Women tended to have smaller body surface area (BSA), left ventricular (LV) internal diastolic diameter, and smaller LV outflow tract diameter but were similar to men in terms of LV ejection fraction, AS severity, and patterns of LV remodelling. CONCLUSIONS: Women appeared to have better outcomes compared to men in medically managed severe AS. These gender differences warrant further study and may affect prognosis, follow-up, and timing of valve surgery.

High-Entropy Pyrochlore A2B2O7 with Both Heavy and Light Rare-Earth Elements at the A Site.

A novel class of high-entropy pyrochlore ceramics (HEPCs) with multiple heavy and light rare-earth elements at the A site were successfully synthesized via solid-state reaction. Both the XRD patterns and Raman spectroscopy demonstrated the single pyrochlore structure feature of seven kinds of HEPCs. Electron microscopic images revealed the typical morphology and the homogeneous distribution of all rare-earth elements. It can be concluded that the significance of configuration entropy in the HEPC system has promoted the tervalent lanthanide nuclides to form a single pyrochlore structure. This work is expected to provide guidance for the further design of high-entropy pyrochlore/fluorite ceramics.

Mxenes Derived Laminated and Magnetic Composites with Excellent Microwave Absorbing Performance.

Two dimensional materials have been widely identified as promising microwave absorbers, owing to their large surface area and abundant interfaces. Here, a novel laminated and magnetic composite derived from Mxene was designed and successfully synthesized via facile hydrothermal oxidization of nickel ion intercalated Ti3C2. Highly disordered carbon sheets were obtained by low temperature hydrothermal oxidization, and the in-situ produced TiO2 and NiO nanoparticles embedded closely between them. This layered hybrid exhibits excellent microwave absorbing performance with an effective absorbing bandwidth as high as 11.1 GHz (6.9-18 GHz) and 9 GHz (9-18 GHz) when the thickness is 3 and 2 mm, respectively. Besides the high dielectric loss, magnetic loss and ohmic loss of the composite, the amorphous nature of obtained carbon sheets and multi-reflections between them are believed to play a decisive role in achieving such superior microwave absorbing performance.

Subgroup analysis reveals molecular heterogeneity and provides potential precise treatment for pancreatic cancers.

BACKGROUND: The relationship between molecular heterogeneity and clinical features of pancreatic cancer remains unclear. In this study, pancreatic cancer was divided into different subgroups to explore its specific molecular characteristics and potential therapeutic targets. PATIENTS AND METHODS: Expression profiling data were downloaded from The Cancer Genome Atlas database and standardized. Bioinformatics techniques such as unsupervised hierarchical clustering was used to explore the optimal molecular subgroups in pancreatic cancer. Clinical pathological features and pathways in each subgroup were also analyzed to find out the potential clinical applications and initial promotive mechanisms of pancreatic cancer. RESULTS: Pancreatic cancer was divided into three subgroups based on different gene expression features. Patients included in each subgroup had specific biological features and responded significantly different to chemotherapy. CONCLUSION: Three distinct subgroups of pancreatic cancer were identified, which means that patients in each subgroup might benefit from targeted individual management.

Dependences of microstructure on electromagnetic interference shielding properties of nano-layered Ti3AlC2 ceramics.

The microstructure dependent electromagnetic interference (EMI) shielding properties of nano-layered Ti3AlC2 ceramics were presented in this study by comparing the shielding properties of various Ti3AlC2 ceramics with distinct microstructures. Results indicate that Ti3AlC2 ceramics with dense microstructure and coarse grains are more favourable for superior EMI shielding efficiency. High EMI shielding effectiveness over 40 dB at the whole Ku-band frequency range was achieved in Ti3AlC2 ceramics by microstructure optimization, and the high shielding effectiveness were well maintained up to 600 °C. A further investigation reveals that only the absorption loss displays variations upon modifying microstructure by allowing more extensive multiple reflections in coarse layered grains. Moreover, the absorption loss of Ti3AlC2 was found to be much higher than those of highly conductive TiC ceramics without layered structure. These results demonstrate that nano-layered MAX phase ceramics are promising candidates of high-temperature structural EMI shielding materials and provide insightful suggestions for achieving high EMI shielding efficiency in other ceramic-based shielding materials.

Lymph node segmentation by dynamic programming and active contours.

PURPOSE: Enlarged lymph nodes are indicators of cancer staging, and the change in their size is a reflection of treatment response. Automatic lymph node segmentation is challenging, as the boundary can be unclear and the surrounding structures complex. This work communicates a new three-dimensional algorithm for the segmentation of enlarged lymph nodes. METHODS: The algorithm requires a user to draw a region of interest (ROI) enclosing the lymph node. Rays are cast from the center of the ROI, and the intersections of the rays and the boundary of the lymph node form a triangle mesh. The intersection points are determined by dynamic programming. The triangle mesh initializes an active contour which evolves to low-energy boundary. Three radiologists independently delineated the contours of 54 lesions from 48 patients. Dice coefficient was used to evaluate the algorithm's performance. RESULTS: The mean Dice coefficient between computer and the majority vote results was 83.2%. The mean Dice coefficients between the three radiologists' manual segmentations were 84.6%, 86.2%, and 88.3%. CONCLUSIONS: The performance of this segmentation algorithm suggests its potential clinical value for quantifying enlarged lymph nodes.

Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data.

PURPOSE: Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making. METHODS: To assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis. RESULTS: On the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between -36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between -50.5 to 701.5%, and the repeat difference for each institution ranged between -37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively. CONCLUSION: Analysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi-site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.

Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC.

Medical imaging plays a fundamental role in oncology and drug development, by providing a non-invasive method to visualize tumor phenotype. Radiomics can quantify this phenotype comprehensively by applying image-characterization algorithms, and may provide important information beyond tumor size or burden. In this study, we investigated if radiomics can identify a gefitinib response-phenotype, studying high-resolution computed-tomography (CT) imaging of forty-seven patients with early-stage non-small cell lung cancer before and after three weeks of therapy. On the baseline-scan, radiomic-feature Laws-Energy was significantly predictive for EGFR-mutation status (AUC = 0.67, p = 0.03), while volume (AUC = 0.59, p = 0.27) and diameter (AUC = 0.56, p = 0.46) were not. Although no features were predictive on the post-treatment scan (p > 0.08), the change in features between the two scans was strongly predictive (significant feature AUC-range = 0.74-0.91). A technical validation revealed that the associated features were also highly stable for test-retest (mean ± std: ICC = 0.96 ± 0.06). This pilot study shows that radiomic data before treatment is able to predict mutation status and associated gefitinib response non-invasively, demonstrating the potential of radiomics-based phenotyping to improve the stratification and response assessment between tyrosine kinase inhibitors (TKIs) sensitive and resistant patient populations.

Prevalence, clinical and echocardiographic characteristics of various flow and gradient patterns in mild or moderate aortic stenosis with normal left ventricular ejection fraction.

BACKGROUND/OBJECTIVES: Paradoxical low-flow aortic stenosis (AS) with preserved left ventricular ejection fraction (LVEF) has only been described in severe AS. Controversy surrounds prognosis and management but no studies have reported this phenomenon in mild or moderate AS. We investigated the prevalence of flow and gradient patterns in this population, characterising their clinical and echocardiographic profile. METHODS: Consecutive subjects (n=1362) with isolated AS: mild (n=462, aortic valve area≥1.5cm(2), 2.5m/s

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

RATIONALE AND OBJECTIVES: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

Reproducibility of radiomics for deciphering tumor phenotype with imaging.

Radiomics (radiogenomics) characterizes tumor phenotypes based on quantitative image features derived from routine radiologic imaging to improve cancer diagnosis, prognosis, prediction and response to therapy. Although radiomic features must be reproducible to qualify as biomarkers for clinical care, little is known about how routine imaging acquisition techniques/parameters affect reproducibility. To begin to fill this knowledge gap, we assessed the reproducibility of a comprehensive, commonly-used set of radiomic features using a unique, same-day repeat computed tomography data set from lung cancer patients. Each scan was reconstructed at 6 imaging settings, varying slice thicknesses (1.25 mm, 2.5 mm and 5 mm) and reconstruction algorithms (sharp, smooth). Reproducibility was assessed using the repeat scans reconstructed at identical imaging setting (6 settings in total). In separate analyses, we explored differences in radiomic features due to different imaging parameters by assessing the agreement of these radiomic features extracted from the repeat scans reconstructed at the same slice thickness but different algorithms (3 settings in total). Our data suggest that radiomic features are reproducible over a wide range of imaging settings. However, smooth and sharp reconstruction algorithms should not be used interchangeably. These findings will raise awareness of the importance of properly setting imaging acquisition parameters in radiomics/radiogenomics research.

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.

Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 µl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

Hybrid detection of lung nodules on CT scan images.

PURPOSE: The diversity of lung nodules poses difficulty for the current computer-aided diagnostic (CAD) schemes for lung nodule detection on computed tomography (CT) scan images, especially in large-scale CT screening studies. We proposed a novel CAD scheme based on a hybrid method to address the challenges of detection in diverse lung nodules. METHODS: The hybrid method proposed in this paper integrates several existing and widely used algorithms in the field of nodule detection, including morphological operation, dot-enhancement based on Hessian matrix, fuzzy connectedness segmentation, local density maximum algorithm, geodesic distance map, and regression tree classification. All of the adopted algorithms were organized into tree structures with multi-nodes. Each node in the tree structure aimed to deal with one type of lung nodule. RESULTS: The method has been evaluated on 294 CT scans from the Lung Image Database Consortium (LIDC) dataset. The CT scans were randomly divided into two independent subsets: a training set (196 scans) and a test set (98 scans). In total, the 294 CT scans contained 631 lung nodules, which were annotated by at least two radiologists participating in the LIDC project. The sensitivity and false positive per scan for the training set were 87% and 2.61%. The sensitivity and false positive per scan for the testing set were 85.2% and 3.13%. CONCLUSIONS: The proposed hybrid method yielded high performance on the evaluation dataset and exhibits advantages over existing CAD schemes. We believe that the present method would be useful for a wide variety of CT imaging protocols used in both routine diagnosis and screening studies.

Unfolding grain size effects in barium titanate ferroelectric ceramics.

Grain size effects on the physical properties of polycrystalline ferroelectrics have been extensively studied for decades; however there are still major controversies regarding the dependence of the piezoelectric and ferroelectric properties on the grain size. Dense BaTiO3 ceramics with different grain sizes were fabricated by either conventional sintering or spark plasma sintering using micro- and nano-sized powders. The results show that the grain size effect on the dielectric permittivity is nearly independent of the sintering method and starting powder used. A peak in the permittivity is observed in all the ceramics with a grain size near 1 µm and can be attributed to a maximum domain wall density and mobility. The piezoelectric coefficient d33 and remnant polarization Pr show diverse grain size effects depending on the particle size of the starting powder and sintering temperature. This suggests that besides domain wall density, other factors such as back fields and point defects, which influence the domain wall mobility, could be responsible for the different grain size dependence observed in the dielectric and piezoelectric/ferroelectric properties. In cases where point defects are not the dominant contributor, the piezoelectric constant d33 and the remnant polarization Pr increase with increasing grain size.