Impact of heterogeneous reuse on heritage value under the perspective of scale subdivision - Two modern theatres in Nanjing as examples

Heterogeneous reuse is a type of reuse where the demand for new functional space differs significantly from the supply of the original space. It causes changes in the building morphology at all scale levels, which in turn has an impact on heritage value. Heterogeneous reuse is prevalent in the conservation of modern Chinese architecture, this article analyses the mechanism of heritage value change under different intervention methods, taking the Shengli Theatre and the Dahua Theatre in Nanjing as examples. Firstly, the value-bearing areas are identified by overall value assessment; secondly, the value of each value-bearing area at each scale level is determined by combining the theory of scale subdivision; Thirdly, influences of different interventions on the value of overall building and the value bearing parts are calculated by comparing the value changes before and after conservation and renovation. This research reveals that heterogeneous reuse often leads to a decline in the emotional and cultural value of built heritage, but enhances the use value. The overall value may also increase if done in appropriate ways. Through this article, the potential of typomorphology in the study of heterogeneous reuse is expanded, and through the integration of scale subdivision and value assessment, the specific heritage values at each scale level can be fine-expressed, while the effectiveness of various interventions can be reasonably evaluated

GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials

Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-world problems on graph-structured data. However, these models usually have at least one of four fundamental limitations: over-smoothing, over-fitting, difficult to train, and strong homophily assumption. For example, Simple Graph Convolution (SGC) is known to suffer from the first and fourth limitations. To tackle these limitations, we identify a set of key designs including (D1) dilated convolution, (D2) multi-channel learning, (D3) self-attention score, and (D4) sign factor to boost learning from different types (i.e. homophily and heterophily) and scales (i.e. small, medium, and large) of networks, and combine them into a graph neural network, GPNet, a simple and efficient one-layer model. We theoretically analyze the model and show that it can approximate various graph filters by adjusting the self-attention score and sign factor. Experiments show that GPNet consistently outperforms baselines in terms of average rank, average accuracy, complexity, and parameters on semi-supervised and full-supervised tasks, and achieves competitive performance compared to state-of-the-art model with inductive learning task.Comment: 15 pages, 15 figure

Suppression of MyD88-dependent signaling alleviates neuropathic pain induced by peripheral nerve injury in the rat

Abstract Background MyD88 is the adaptor protein of MyD88-dependent signaling pathway of TLRs and IL-1 receptor and regulates innate immune response. However, it was not clear whether and how MyD88 and related signaling pathways in the dorsal root ganglion (DRG) and spinal dorsal horn (SDH) are involved in neuropathic pain. Methods Chronic constriction injury (CCI) was used to induce neuropathic pain in the rat. The expression of MyD88, TRIF, IBA1, and GFAP was detected with immunofluorescent staining and Western blot. The expression of interleukin-1 beta (IL-1β), high mobility group box 1 (HMGB1), NF-κB-p65, phosphorylated NF-κB-p65, ERK, phosphorylated ERK, and tumor necrosis factor-alpha (TNF-α) was detected with Western blot. Pain-related behavioral effects of MyD88 homodimerization inhibitory peptide (MIP) were accessed up to 3 weeks after intrathecal administration. Results Peripheral nerve injury significantly increased the protein level of MyD88 in the DRG and SDH, but had no effect on TRIF. MyD88 was found partly distributed in the nociceptive neurons in the DRGs and the astrocytes and microglia in the SDH. HMGB1 and IL-1β were also found upregulated in nociceptive pathways of CCI rats. Intrathecal application of MIP significantly alleviated mechanical and thermal hyperalgesia in the CCI rats and also reversed CCI-induced upregulation of MyD88 in both DRG and SDH. Further investigation revealed that suppression of MyD88 protein reduced the release of TNF-α and glial activation in the SDH in the CCI rats. Conclusions MyD88-dependent TIR pathway in the DRG and SDH may play a role in CCI-induced neuropathic pain. MyD88 might serve as a potential therapeutic target for neuropathic pain

An explainable Transformer-based deep learning model for the prediction of incident heart failure

Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction but remain unexplainable hampering their wider use in medical practice. We developed a novel Transformer deep-learning model for more accurate and yet explainable prediction of incident heart failure involving 100,071 patients from longitudinal linked electronic health records across the UK. On internal 5-fold cross validation and held-out external validation, our model achieved 0.93 and 0.93 area under the receiver operator curve and 0.69 and 0.70 area under the precision-recall curve, respectively and outperformed existing deep learning models. Predictor groups included all community and hospital diagnoses and medications contextualised within the age and calendar year for each patient's clinical encounter. The importance of contextualised medical information was revealed in a number of sensitivity analyses, and our perturbation method provided a way of identifying factors contributing to risk. Many of the identified risk factors were consistent with existing knowledge from clinical and epidemiological research but several new associations were revealed which had not been considered in expert-driven risk prediction models