Activity

29 (1.03, 1.61) per 6.3µg/m

for PM

, 1.16 (1.07, 1.27) per 8.2µg/m

for NO

, and 1.12 (1.00, 1.25) per 10dB for L

. The HR for NO

remained unchanged after adjustment for either PM

or L

, while the HRs for PM

and L

attenuated to unity after adjustment for NO

.

Long-term exposure to air pollution was associated with adult-asthma incidence independently of road traffic noise, with NO

most relevant. Road traffic noise was not independently associated with adult-asthma incidence.

Long-term exposure to air pollution was associated with adult-asthma incidence independently of road traffic noise, with NO2 most relevant. Road traffic noise was not independently associated with adult-asthma incidence.Polybrominated dibenzo-p-dioxins and furans (PBDD/Fs) are emerging persistent organic pollutants (POPs) that have similar or higher toxicities than the notorious dioxins. Toxicities, formation mechanisms, and environmental fates of PBDD/Fs are lacking because accurate quantification, especially of higher brominated congeners, is challenging. PBDD/F analysis is difficult because of photolysis and thermal degradation and interference from polybrominated diphenyl ethers. Here, literatures on PBDD/F analysis and environmental occurrences are reviewed to improve our understanding of PBDD/F environmental pollution and human exposure levels. Although PBDD/Fs behave similarly to dioxins, different congener profiles between PBDD/Fs and dioxins in the environment indicates their different sources and formation mechanisms. Herein, potential sources and formation mechanisms of PBDD/Fs were critically discussed, and current knowledge gaps and future directions for PBDD/F research are highlighted. An understanding of PBDD/F formation pathways will allow for development of synergistic control strategies for PBDD/Fs, dioxins, and other dioxin-like POPs.Automatic liver and tumor segmentation play a significant role in clinical interpretation and treatment planning of hepatic diseases. To segment liver and tumor manually from the hundreds of computed tomography (CT) images is tedious and labor-intensive; thus, segmentation becomes expert dependent. RO4987655 mouse In this paper, we proposed the multi-scale approach to improve the receptive field of Convolutional Neural Network (CNN) by representing multi-scale features that extract global and local features at a more granular level. We also recalibrate channel-wise responses of the aggregated multi-scale features that enhance the high-level feature description ability of the network. The experimental results demonstrated the efficacy of a proposed model on a publicly available 3Dircadb dataset. The proposed approach achieved a dice similarity score of 97.13 % for liver and 84.15 % for tumor. The statistical significance analysis by a statistical test with a p-value demonstrated that the proposed model is statistically significant for a significance level of 0.05 (p-value less then 0.05). The multi-scale approach improves the segmentation performance of the network and reduces the computational complexity and network parameters. The experimental results show that the performance of the proposed method outperforms compared with state-of-the-art methods.Neuroimaging data driven machine learning based predictive modeling and pattern recognition has been attracted strongly attention in biomedical sciences. Machine learning based diagnosis techniques are widely applied in diagnosis of neurological diseases. However, machine learning techniques are difficult to effectively extract deep information in neuroimaging data, resulting in low classification accuracy of mental illnesses. To address this problem, we propose a deep learning based automatic diagnosis first-episode psychosis (FEP), bipolar disorder (BD) and healthy controls (HC) method. Specifically, we design a convolutional neural network (CNN) framework to automatically diagnosis based on structural magnetic functional imaging (sMRI). Our dataset consists of 89 FEP patients, 40 BD patients and 83 HC. A three-way classifier (FEP vs. BD vs. HC) and three binary classifiers (FEP vs. BD, FEP vs. HC, BD vs. HC) are trained based on their gray matter volume images. Experiment results show that the performance of CNN-based method outperforms the classic classifiers both in two and three categories classification task. Our research reveals that abnormal gray matter volume is one of the main characteristics for discriminating FEP, BD and HC.Aphasia, one of the most common cognitive impairments after stroke, is commonly considered to be a cortical deficit. However, many studies have reported cases of post subcortical stroke aphasia (PSSA). The pathology and recovery mechanism of PSSA remain unclear. This study aimed to investigate PSSA mechanism through a multimodal magnetic resonance imaging (MRI) approach and a two-session study design (baseline and one month after treatment). Thirty-six PSSA patients and twenty-four matched healthy controls (HC) were included. All patients had subcortical infarctions involving left subcortical white matter for 1 to 6 months. The patients underwent MRI scan and Western Aphasia Battery (WAB) examination before and after one month’s comprehensive treatment. Region-wise lesion-symptom mapping (RLSM), tractography, fractional anisotropy (FA), and amplitude of low-frequency fluctuations (ALFF) analysis were conducted. After MRI preprocessing and exclusion, FA analysis included 35 patients pre-treatment and 16 patients post-treatment. ALFF analysis included 30 patients pre-treatment and 14 patients post-treatment. We found 1) the amount of damage in the left uncinate fasciculus (UF) was associated with WAB aphasia quotient (AQ); 2) the left UF FA and left temporal pole (TP) ALFF were decreased and positively correlated with WAB-AQ, spontaneous speech, and naming in PSSA patients; and 3) PSSA patients showed increased left TP ALFF when their language ability recovered after treatment. The left TP ALFF change was positively correlated with AQ change. Our results demonstrate the importance of left UF and left TP (one of the cortical terminals of the left UF) in PSSA pathology and recovery. These results may further provide support for the disconnection theory in the mechanism of PSSA.