Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring the susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g. in vivo mouse brain data and brains with lesions, which suggests that the network has generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction and high reconstruction speed demonstrate its potential for future applications.Comment: 26 page

Atlastin regulates store-operated calcium entry for nerve growth factor-induced neurite outgrowth

Homotypic membrane fusion of the endoplasmic reticulum (ER) is mediated by a class of dynamin-like GTPases known as atlastin (ATL). Depletion of or mutations in ATL cause an unbranched ER morphology and hereditary spastic paraplegia (HSP), a neurodegenerative disease characterized by axon shortening in corticospinal motor neurons and progressive spasticity of the lower limbs. How ER shaping is linked to neuronal defects is poorly understood. Here, we show that dominant-negative mutants of ATL1 in PC-12 cells inhibit nerve growth factor (NGF)-induced neurite outgrowth. Overexpression of wild-type or mutant ATL1 or depletion of ATLs alters ER morphology and affects store-operated calcium entry (SOCE) by decreasing STIM1 puncta formation near the plasma membrane upon calcium depletion of the ER. In addition, blockage of the STIM1-Orai pathway effectively abolishes neurite outgrowth of PC-12 cells stimulated by NGF. These results suggest that SOCE plays an important role in neuronal regeneration, and mutations in ATL1 may cause HSP, partly by undermining SOCE

Sex differences in non-communicable disease prevalence in China: a cross-sectional analysis of the China Health and Retirement Longitudinal Study in 2011

ObjectivesTo describe the sex differences in the prevalence of non-communicable diseases (NCDs) in adults aged 45 years or older in China.DesignCross-sectional study.SettingNationally representative sample of the Chinese population 2011.Participants8401 men and 8928 women over 45 years of age who participated in the first wave of the China Health and Retirement Longitudinal Study (CHARLS).Outcome measuresSelf-reported data on overall health and diagnosis of hypertension, dyslipidaemia, diabetes, heart disease, stroke, chronic lung disease, cancer or arthritis. Sex differences in NCDs were described using logistic regression to generate odds ratios (OR) with adjustment for sociodemographic factors and health-related behaviours. All analyses were stratified by age group for 45–64-year-old and ≥65-year-old participants.ResultsIn both age groups, men reported better overall health than women. The crude prevalence of heart disease, cancer and arthritis was higher while that of stroke and chronic lung disease was lower in women than in men. After adjustment, ORs (95% CI) for the 45–64 and ≥65 year age groups were 0.70 (0.58 to 0.84) and 0.66 (0.54 to 0.80), respectively, for arthritis for men compared with women. In contrast, ORs were 1.66 (1.09 to 2.52) and 2.12 (1.36 to 3.30) for stroke and 1.51 (1.21 to 1.89) and 1.43 (1.09 to 1.88) for chronic lung disease for men compared with women. ORs for heart disease (0.65 (0.52 to 0.80)) were lower in men than in women only in the 45–64 year age group.ConclusionsOdds of arthritis were lower while those of stroke and chronic lung disease were higher in men than in women in both age groups. However, odds of heart disease were lower in men than in women, but only in the group of individuals aged 45–64 years.</jats:sec

The Development of an Experimental Framework to Explore the Generative Design Preference of a Machine Learning-Assisted Residential Site Plan Layout

Generative design based on machine learning has become an important area of application for artificial intelligence. Regarding the generative design process for residential site plan layouts (hereafter referred to as “RSPLs”), the lack of experimental demonstration begs the question: what are the design preferences of machine learning? In this case, all design elements of the target object need to be extracted as much as possible to conduct experimental studies to produce scientific experimental results. Based on this, the Pix2pix model was used as the test case for Chinese residential areas in this study. An experimental framework of “extract-translate-machine-learning-evaluate” is proposed, combining different machine and manual computations, as well as quantitative and qualitative evaluation techniques, to jointly determine which design elements and their characteristic representations are machine learning design preferences in the field of RSPL. The results show that machine learning can assist in optimizing the design of two particular RSPL elements to conform to residential site layout plans: plaza paving and landscaped green space. In addition, two other major elements, public facilities and spatial structures, were also found to exhibit more significant design preferences, with the largest percentage increase in the number of changes required after machine learning. Finally, the experimental framework established in this study compensates for the lack of consideration that all design elements of a residential area simultaneously utilize the same methodological framework. This can also assist planners in developing solutions that better meet the expectations of residents and can clarify the potential and advantageous directions for the application of machine learning-assisted RSPL