Relationships between building attributes and COVID-19 infection in London

In the UK, all domestic COVID-19 restrictions have been removed since they were introduced in March 2020. After illustrating the spatial-temporal variations in COVID-19 infection rates across London, this study then particularly aimed to examine the relationships of COVID-19 infection rates with building attributes, including building density, type, age, and use, since previous studies have shown that the built environment plays an important role in public health. Multisource data from national health services and the London Geomni map were processed with GIS techniques and statistically analysed. From March 2020 to April 2022, the infection rate of COVID-19 in London was 3159.28 cases per 10,000 people. The spatial distribution across London was uneven, with a range from 1837.88 to 4391.79 per 10,000 people. During the whole COVID-19 control period, it was revealed that building attributes played a significant role in COVID-19 infection. It was noted that higher building density areas had lower COVID-19 infection rates in London. Moreover, a higher percentage of historic or flat buildings tended to lead to a decrease in infection rates. The percentage of residential buildings had a positive relationship with the infection rate. Variations in the infection rate were more sensitive to building type; in particular, the percentage of residents living in flats contributed the most to variations in COVID-19 infection rates, with a value of 2.5%. This study is expected to provide support for policy and practice towards pandemic-resilient architectural design

Climate impacts of U.S. forest loss span net warming to net cooling

Storing carbon in forests is a leading land-based strategy to curb anthropogenic climate change, but its planetary cooling effect is opposed by warming from low albedo. Using detailed geospatial data from Earth-observing satellites and the national forest inventory, we quantify the net climate effect of losing forest across the conterminous United States. We find that forest loss in the intermountain and Rocky Mountain West causes net planetary cooling but losses east of the Mississippi River and in Pacific Coast states tend toward net warming. Actual U.S. forest conversions from 1986 to 2000 cause net cooling for a decade but then transition to a large net warming over a century. Avoiding these forest conversions could have yielded a 100-year average annual global cooling of 0.00088°C. This would offset 17% of the 100-year climate warming effect from a single year of U.S. fossil fuel emissions, underscoring the scale of the mitigation challenge. Supporting data is also available as supplementary download

Data Augmentation for Deep Graph Learning: A Survey

Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To address the data noise and data scarcity issues in deep graph learning, the research on graph data augmentation has intensified lately. However, conventional data augmentation methods can hardly handle graph-structured data which is defined in non-Euclidean space with multi-modality. In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in different deep graph learning problems. Specifically, we first propose a taxonomy for graph data augmentation techniques and then provide a structured review by categorizing the related work based on the augmented information modalities. Moreover, we summarize the applications of graph data augmentation in two representative problems in data-centric deep graph learning: (1) reliable graph learning which focuses on enhancing the utility of input graph as well as the model capacity via graph data augmentation; and (2) low-resource graph learning which targets on enlarging the labeled training data scale through graph data augmentation. For each problem, we also provide a hierarchical problem taxonomy and review the existing literature related to graph data augmentation. Finally, we point out promising research directions and the challenges in future research.Comment: Accepted by SIGKDD Explorations Paper list: https://github.com/kaize0409/awesome-graph-data-augmentaio

Using multi-sourced big data to correlate sleep deprivation and road traffic noise: A US county-level ecological study

Background: Road traffic noise is a serious public health problem globally as it has adverse psychosocial and physiologic effects (i.e., sleep). Since previous studies mainly focused on individual levels, we aim to examine associations between road traffic noise and sleep deprivation on a large scale; namely, the US at county level. Methods: Information from a large-scale sleep survey and national traffic noise map, both obtained from Government's open data, were utilized and processed with GIS techniques. To examine the associations between traffic noise and sleep deprivation, we used a hierarchical Bayesian spatial modelling framework to simultaneously adjust for multiple socioeconomic factors while accounting for spatial correlation. Findings: With 62.90% of people not getting enough sleep, a 10 dBA increase in average sound-pressure level (SPL) or SPL of the relatively noisy area in a county, was associated with a 49% (OR: 1.49; 95% CrIs:1.19–1.86) or 8% (1.08; 1.00–1.16) increase in the odds of a person in a particular county not getting enough sleep. A 10% increase in noise exposure area or population ratio was associated with a 3% (1.03; 1.01–1.06) or 4% (1.04; 1.02–1.06) increase in the odds of a person within a county not getting enough sleep. Interpretation: Traffic noise can contribute to variations in sleep deprivation among counties. This study suggests that policymakers could set up different noise-management strategies for relatively quiet and noisy areas (i.e., different limiting SPLs) and incorporate geo-spatial noise indicators, such as exposure population or area ratio. Furthermore, urban planners should consider urban sprawl patterns differently

Feasibility of In-Situ Aeration of Old Dumping Ground for Land Reclamation

Dumping grounds are characterized by the absence of engineering controls such as base liners and cover layer. Consequently, these dumping grounds present risks for surrounding resources such as soil, groundwater and air. The concern for groundwater contamination by leachate from tropical dumping grounds is heightened due to the greater amounts of rainfall and subsequent infiltration and percolation through the waste mass. The emergent demand for old dumping grounds reclamation drives the need to employ remediation technologies. Generally, in-situ aeration is a remediation method that promotes aerobic conditions in the later stage of dumping ground. It accelerates carbon transfer, reduces remaining organic load, and generally shortens the post closure period. However, high rainfall in tropical areas straitens this technique. For example, pollutants could be easily flushed out and more energy should be required to overcome hydrostatic pressure. Although heavy rainfall could supply sufficient water to the substrate and accelerate degradation of organic matter, it may inhibit aerobic activities due to limited air transfer. The waste characterization from Lorong Halus Dumping Ground (closed dumping ground in Singapore) showed that the waste materials were stabilized after 22 years closure. According to the Waste Acceptance Criteria set by European Communities Council, the waste materials could be classified as inert wastes. One interesting finding was that leachate layer detected was about of 5 - 8 meter depth, which entirely soaked the waste materials. Hence, the reclamation design and operation should be carefully adjusted according to these characters. Lorong Halus Dumping Ground case study can provide a guideline for other tropical closed landfills or dumping grounds

Galectin-3 regulates intracellular trafficking of EGFR through Alix and promotes keratinocyte migration.

The EGFR-mediated signaling pathways are important in a variety of cellular processes, including cell migration and wound re-epithelialization. Intracellular trafficking of EGFR is critical for maintaining EGFR surface expression. Galectin-3, a member of an animal lectin family, has been implicated in a number of physiological and pathological processes. Through studies of galectin-3-deficient mice and cells isolated from these mice, we demonstrated that the absence of galectin-3 impairs keratinocyte migration and skin wound re-epithelialization. We have linked this pro-migratory function to a crucial role of cytosolic galectin-3 in controlling intracellular trafficking and cell surface expression of EGFR after EGF stimulation. Without galectin-3, the surface levels of EGFR are markedly reduced, and the receptor accumulates diffusely in the cytoplasm. This is associated with reduced rates of both endocytosis and recycling of the receptor. We have provided evidence that this previously unreported function of galectin-3 may be mediated through interaction with its binding partner Alix, which is a protein component of the ESCRT (endosomal sorting complex required for transport) machinery. Our results suggest that galectin-3 is potentially a critical regulator of a number of important cellular responses through its intracellular control of trafficking of cell surface receptors

Genomic Features and Clinical Characteristics of Adolescents and Young Adults With Cholangiocarcinoma

Background: Adolescents and young adults (AYAs) diagnosed with cancer between ages 15 and 45 years may exhibit unique biologic and genomic characteristics as well as clinical features, resulting in differences in clinical characters and drug resistance. However, compared to other solid cancers, relatively few studies have been conducted in this age group in cholangiocarcinoma (CCA). This study is performed to investigate the clinical and molecular features of AYAs with CCA. Methods: Three cohorts, including the external dataset (TCGA and MSKCC) and the perihilar CCA databank of Chinese tertiary hospitals, were contained in this study. Pathway and process enrichment analysis had been carried out with the following ontology sources: KEGG Pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways, and CORUM. Metascape and GEPIA datasets were used for bioinformatic analysis. P < 0.05 was considered statistically significant. All statistical analyses were performed with GraphPad Prism (version 7.0; GraphPad Software, La Jolla, California) and R studio (version 3.6.1; R studio, Boston, Massachusetts). Results: Compared to older adults, AYAs with CCA presented with worse overall survival, although the difference was not significant. Specific to patients with stage IV CCAs who underwent chemotherapy, AYAs were associated with significantly poorer overall survival (OS) (p = 0.03, hazards ratio (HR) 3.01, 95% confidence interval (CI) 1.14-4.91). From the anatomical perspective, more extrahepatic CCA was detected in the AYA group. Microsatellite instability (MSI) occurred in 3% of older patients in the present study. Nevertheless, none of the AYAs had MSI status. In this study, AYAs gained an enhanced frequency of additional sex combs like 1 (ASXL1) (p = 0.02) and KMT2C (p = 0.02) mutation than their older counterparts. Besides ASXL1 and KMT2C, the genes enriched in AYAs with CCA were analyzed by pathway and process enrichment analysis. And those genes were found to be associated with poorer differentiation, deubiquitination, and WNT signal pathway. Moreover, AYAs were relevant to poor differentiation and advanced tumor stage. Conclusion: This study offered a preliminary landscape of the clinical and molecular features of early-onset biliary cancers. Further studies including more samples are essential to investigate whether ASXL1 and KMT2C could be considered as potentially targetable genomic signatures for young patients