From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e.g., knowledge distillation or reinvented representation forms) to compensate for reduced dimension. However, such operations introduce complicated computations and model designs that may not benefit large knowledge graphs. To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures. We view all entity representations as a single-layer embedding network, and conventional KGE methods that adopt high-dimensional entity representations equal widening the embedding network to gain expressiveness. To achieve parameter efficiency, we instead propose a deeper embedding network for entity representations, i.e., a narrow entity embedding layer plus a multi-layer dimension lifting network (LiftNet). Experiments on three public datasets show that by integrating LiftNet, four conventional KGE methods with 16-dimensional representations achieve comparable link prediction accuracy as original models that adopt 512-dimensional representations, saving 68.4% to 96.9% parameters

A spacematrix and clustering approach to understanding the morphology of Singapore’s Housing Development Board (HDB) estates

Urban morphology profoundly influences city planning and experiences significant transformations as cities evolve. This paper investigates paradigm shifts in block-level planning through a case study of Singapore, a city celebrated for its precision in urban planning and swift transformation. Integrating urban morphology theories with empirical data, we explore Singapore’s block-level urban form across various stages of development. Utilising a Spacematrix approach alongside a clustering analysis of urban blocks, we categorise Singapore’s towns into four distinct clusters: Suburban, Balanced Mix, Dense Urban, and Vertical Growth, each reflecting unique density patterns and building forms. This clustering reveals how Singapore’s planning ideologies have transitioned from maximising space utilisation to prioritising sustainability and quality of living. This signifies a paradigm shift towards a comprehensive and inclusive urban design ethos. The paper contributes to the urban planning discourse by underscoring the technological advancements, especially with merging spatial data and GIS, in shaping modern urban analytics and planning. The insights from the clustering analysis enhance our understanding of Singapore’s exceptional urban path and offer valuable perspectives for other metropolises navigating the complexities of urban expansion and sustainability

Machine learning-based characterisation of urban morphology with the street pattern

Streets are a crucial part of the built environment, and their layouts, the street patterns, are widely researched and contribute to a quantitative understanding of urban morphology. However, traditional street pattern analysis only considers a few broadly defined characteristics. It uses administrative boundaries and grids as units of analysis that fail to encompass the diversity and complexity of street networks. To address these challenges, this research proposes a machine learning-based approach to automatically recognise street patterns that employs an adaptive analysis unit based on street-based local areas (SLAs). SLAs use a network partitioning technique that can adapt to distinct street networks, making it particularly suitable for different urban contexts. By calculating several streets’ network metrics and performing a hierarchical clustering method, streets with similar characters are grouped under the same street pattern. A case study is carried out in six cities worldwide. The results show that street pattern types are rather diverse and hierarchical, and categorising them into clearly demarcated taxonomy is challenging. The study derives a set of new morphometrics-based street patterns with four major types that resemble conventional street patterns and eleven sub-types to significantly increase their diversity for broader coverage of urban morphology. The new patterns capture urban structural differences across cities, such as the urban-suburban division and the number of urban centres present. In conclusion, the proposed machine learning-based morphometric street pattern to characterise urban morphology has an enhanced ability to encompass more information from the built environment while maintaining the intuitiveness of using patterns

Toll-Like Receptors, Associated Biological Roles, and Signaling Networks in Non-Mammals

The innate immune system is the first line of defense against pathogens, which is initiated by the recognition of pathogen-associated molecular patterns (PAMPs) and endogenous damage-associated molecular patterns (DAMPs) by pattern recognition receptors (PRRs). Among all the PRRs identified, the toll-like receptors (TLRs) are the most ancient class, with the most extensive spectrum of pathogen recognition. Since the first discovery of Toll in Drosophila melanogaster, numerous TLRs have been identified across a wide range of invertebrate and vertebrate species. It seems that TLRs, the signaling pathways that they initiate, or related adaptor proteins are essentially conserved in a wide variety of organisms, from Porifera to mammals. Molecular structure analysis indicates that most TLR homologs share similar domain patterns and that some vital participants of TLR signaling co-evolved with TLRs themselves. However, functional specification and emergence of new signaling pathways, as well as adaptors, did occur during evolution. In addition, ambiguities and gaps in knowledge still exist regarding the TLR network, especially in lower organisms. Hence, a systematic review from the comparative angle regarding this tremendous signaling system and the scenario of evolutionary pattern across Animalia is needed. In the current review, we present overview and possible evolutionary patterns of TLRs in non-mammals, hoping that this will provide clues for further investigations in this field

18F-FDG PET/CT revealed sporadic schwannomatosis involving the lumbar spinal canal and both lower limbs: a case report

Schwannomatosis is a rare autosomal dominant hereditary syndrome disease characterized by multiple schwannomas throughout the body, without bilateral vestibular schwannoma or dermal schwannoma. The most common location of schwannomatosis is the head and neck, as well as the limbs, while multiple schwannomas in the lumbosacral canal and lower extremities are relatively rare. In this study, we report a 79-year-old woman diagnosed with schwannomatosis. MRI and contrast-enhanced imaging revealed multiple schwannomas in both lower extremities. An 18F-FDG PET/CT examination revealed that in addition to multiple tumors with increased 18F-FDG uptake in both lower extremities, there was also an increased 18F-FDG uptake in a mass in the lumbosacral canal. These masses were confirmed to be schwannomas by pathology after surgery or biopsy. 18F-FDG PET/CT findings of schwannomas were correlated with MRI and pathological components. Antoni A area rich in tumor cells showed significant enhancement on contrast-enhanced T1WI, and PET/CT showed increased uptake of 18F-FDG in the corresponding area, while Antoni B region rich in mucus showed low enhancement on contrast-enhanced T1WI, accompanied by a mildly increased 18F-FDG uptake

Multimodal imaging findings of primary liver clear cell carcinoma: a case presentation

Primary clear cell carcinoma of liver (PCCCL) is a special and relatively rare subtype of hepatocellular carcinoma (HCC), which is more common in people over 50 years of age, with a preference for men and a history of hepatitis B or C and/or cirrhosis. Herein, we present a case of a 60-year-old woman who came to our hospital for medical help with right upper abdominal pain. The imaging examination showed a low-density mass in the right lobe of his liver. In contrast enhanced computed tomography (CT) or T1-weighted imaging, significant enhancement can appear around the tumor during the arterial phase, and over time, the degree of enhancement of the tumor gradually decreases. The lession showed obviously increased fluorine-18 fluorodeoxyglucose (18F-FDG) uptake on positron emission tomography/CT. These imaging findings contribute to the diagnosis of PCCCL and differentiate it from other types of liver tumors

Ustekinumab treats psoriasis by suppressing RORC and T-box but its suppression of GATA restrains its efficacy

Psoriasis is a T-cell mediated disease that involves IL-23/Th17 and IL-12/Th1 axes. Ustekinumab, a fully human monoclonal antibody targeting the p40 subunit of both IL-12 and IL-23, has proven to be efficient and safe for treating patients with psoriasis. Yet, there have been no reports with human skin/blood samples that would elucidate the molecular mechanisms by which ustekinumab calms psoriasis skin lesions. To investigate the efficacy and molecular pathway (RORC, t-BOX and GATA) of ustekinumab in treating patients with psoriasis skin lesions. A total of 30 patients with psoriasis were randomized into placebo group and treatment group. PASI of each patient was calculated at 0, 12 and 24 weeks post-treatment. The mRNA levels of RORC, t-BOX and GATA in peripheral blood mononuclear cells separated from patients’ whole blood were analyzed using qPCR. Decreased mRNA of RORC, t-BOX and GATA were observed after continuous injections, indicating that ustekinumab exerts its effect by interacting with these molecules; while no significant difference in foxp3 mRNA levels were found between placebo group and treatment group

Mapping street patterns with network science and supervised machine learning

This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street metrics and GIS, the study analysed the urban form through the random forest method, which reveals the predictive features of urban patterns and enables a deeper understanding of the spatial structures of cities. The findings showed distinctive spatial structures, such as ring formations and urban cores, indicating stages of urban development and socioeconomic narratives. It also showed that the unit of analysis has a major impact on the identification and study of street patterns. Concluding that machine learning is a critical tool in urban morphology, the research suggests that future studies should expand this framework to include more cities and urban elements. This would enhance the predictive modelling of urban growth and inform sustainable, human-centric urban planning. The implications of this study are significant for policymakers and urban planners seeking to harness data-driven insights for the development of cities