Thalamic T2 Hyperintensities and Cognitive Function in Chinese Children With NF-1

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Reconstruction of FRI signals using deep neural network approaches

Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited continuous signals that have a small number of free parameters, such as a stream of Diracs. The task of reconstructing FRI signals from discrete samples is often transformed into a spectral estimation problem and solved using Prony's method and matrix pencil method which involve estimating signal subspaces. They achieve an optimal performance given by the Cramér-Rao bound yet break down at a certain peak signal-to-noise ratio (PSNR). This is probably due to the so-called subspace swap event. In this paper, we aim to alleviate the subspace swap problem and investigate alternative approaches including directly estimating FRI parameters using deep neural networks and utilising deep neural networks as denoisers to reduce the noise in the samples. Simulations show significant improvements on the breakdown PSNR over existing FRI methods, which still outperform learning-based approaches in medium to high PSNR regimes

Factors affecting ventilation effectiveness in SARS wards

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Neural heterogeneity promotes robust learning

The brain has a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that it substantially improved task performance. Learning was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks closely matches those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments. Summary Neural heterogeneity is metabolically efficient for learning, and optimal parameter distribution matches experimental data

Learning-based reconstruction of FRI signals

Finite Rate of Innovation (FRI) sampling theory enables reconstruction of classes of continuous non-bandlimited signals that have a small number of free parameters from their low-rate discrete samples. This task is often translated into a spectral estimation problem that is solved using methods involving estimating signal subspaces, which tend to break down at a certain peak signal-to-noise ratio (PSNR). To avoid this breakdown, we consider alternative approaches that make use of information from labelled data. We propose two model-based learning methods, including deep unfolding the denoising process in spectral estimation, and constructing an encoder-decoder deep neural network that models the acquisition process. Simulation results of both learning algorithms indicate significant improvements of the breakdown PSNR over classical subspace-based methods. While the deep unfolded network achieves similar performance as the classical FRI techniques and outperforms the encoder-decoder network in the low noise regimes, the latter allows to reconstruct the FRI signal even when the sampling kernel is unknown. We also achieve competitive results in detecting pulses from in vivo calcium imaging data in terms of true positive and false positive rate while providing more precise estimations

Combination of SAHA and bortezomib up-regulates CDKN2A and CDKN1A and induces apoptosis of Epstein-Barr virus-positive Wp-restricted Burkitt lymphoma and lymphoblastoid cell lines

Epstein-Barr virus (EBV) latent proteins exert anti-apoptotic effects on EBV-transformed lymphoid cells by down-regulating BCL2L11 (BIM), CDKN2A (p16(INK4A) ) and CDKN1A (p21(WAF1) ). However, the potential therapeutic effects of targeting these anti-apoptotic mechanisms remain unexplored. Here, we tested both in vitro and in vivo effects of the combination of histone deacetylase (HDAC) and proteasome inhibitors on the apoptosis of six endemic Burkitt lymphoma (BL) lines of different latency patterns (types I and III and Wp-restricted) and three lymphoblastoid cell lines (LCLs). We found that the combination of HDAC and proteasome inhibitors (e.g. SAHA/bortezomib) synergistically induced the killing of Wp-restricted and latency III BL and LCLs but not latency I BL cells. The synergistic killing was due to apoptosis, as evidenced by the high percentage of annexin V positivity and strong cleavage of PARP1 (PARP) and CASP3 (caspase-3). Concomitantly, SAHA/bortezomib up-regulated the expression of CDKN2A and CDKN1A but did not affect the level of BCL2L11 or BHRF1 (viral homologue of BCL2). The apoptotic effects were dependent on reactive oxygen species generation. Furthermore, SAHA/bortezomib suppressed the growth of Wp-restricted BL xenografts in nude mice. This study provides the rationale to test the novel application of SAHA/bortezomib on the treatment of EBV-associated Wp-restricted BL and post-transplant lymphoproliferative disorder.postprin

Deep vein thrombosis and pulmonary embolism in the Chinese population

Deep vein thrombosis and pulmonary embolism is a well-recognised major health problem in the West. There is a deep-rooted belief among clinicians that deep vein thrombosis is rare in Asians, particularly in the Chinese population. However, it appears that the incidence of venous thrombosis and pulmonary embolism is increasing in Chinese patients. Prophylaxis reduces the incidence of venous thrombosis by 66% and of pulmonary embolism by 50%Ը? prophylaxis should therefore be considered for Chinese patients who have a high risk of developing postoperative deep vein thrombosis. This report reviews the current literature on this subject.published_or_final_versio

Mapping radiation dose distribution on the Fractional Anisotropy Map: application in the assessment of treatment-induced white matter injury

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Susceptibility of the optic nerve and the involvement of retrograde neuronal degeneration in a delayed radiation induced injury model: evidence from a diffusion tensor imaging study

Animal Models of White Matter Disease & Neurodegeneration: No. 469In the present study, we evaluated changes of multiple white matter tracts following radiation using diffusion tensor imaging. A novel finding of severe changes in FA in the contralateral optic nerve as compared to the ipsilateral optic nerve was observed, and these changes were confirmed by histological evaluation. These findings cannot be explained by difference in radiation dose and suggests, for the first time, an important role of retrograde neuronal degeneration in the underlying mechanism for radiation induced injury to the visual pathway. The results also suggest susceptibility of the optic nerve relative to the cerebral peduncle.postprin

Regional white matter anisotropy and general intelligence in preterm born children: a voxelwise analysis

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