Three-dimensional (3D) printing is an advanced technology for accurately understanding anatomy and supporting the successful surgical management of complex congenital heart disease (CHD). We aimed to evaluate whether our super-flexible 3D heart models could facilitate preoperative decision-making and surgical simulation for complex CHD. The super-flexible heart models were fabricated by stereolithography 3D printing of the internal and external contours of the heart from cardiac computed tomography (CT) data, followed by vacuum casting with a polyurethane material similar in elasticity to a child's heart.
View Article and Find Full Text PDFBackground: Cognitive impairment is a cardinal feature in patients with schizophrenia and leads to poor social functioning. Recently, the treatment of schizophrenia has evolved to include the goal of improving quality of life (QoL). However, most of the factors influencing subjective QoL are unknown.
View Article and Find Full Text PDFSparse Bayesian learning has promoted many effective frameworks of brain activity decoding for the brain-computer interface, including the direct reconstruction of muscle activity using brain recordings. However, existing sparse Bayesian learning algorithms mainly use Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to large performance degradation for sparse Bayesian learning algorithms.
View Article and Find Full Text PDFPremise: Nelumbo nucifera is one of several plant species with flowers that typically open in the early morning and close by noon. This movement normally repeats for 3 days, with all petals falling off on day 4. However, detailed observations of flower movement in Nelumbo species are limited.
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