Spatial interaction and functional status of CD68+SHP2+ macrophages in tumor microenvironment correlate with overall survival of NSCLC.

Background: Tumor-associated macrophages (TAMs) constitute a plastic and heterogeneous cell population of the tumor microenvironment (TME) that can regulate tumor proliferation and support resistance to therapy, constituting promising targets for the development of novel anticancer agents. Our previous results suggest that SHP2 plays a crucial role in reprogramming the phenotype of TAMs. Thus, we hypothesized that SHP2+ TAM may predict the treatment efficacy of non-small cell lung cancer NSCLC patients as a biomarker. Methods: We analyzed cancer tissue samples from 79 NSCLC patients using multiplex fluorescence (mIF) staining to visualize various SHP-2+ TAM subpopulations (CD68+SHP2+, CD68+CD86+, CD68 + 206+, CD68+ CD86+SHP2+, CD68+ CD206+SHP2+) and T cells (CD8+ Granzyme B +) of immune cells. The immune cells proportions were quantified in the tumor regions (Tumor) and stromal regions (Stroma), as well as in the overall tumor microenvironment (Tumor and Stroma, TME). The analysis endpoint was overall survival (OS), correlating them with levels of cell infiltration or effective density. Cox regression was used to evaluate the associations between immune cell subsets infiltration and OS. Correlations between different immune cell subsets were examined by Spearman's tests. Results: In NSCLC, the distribution of different macrophage subsets within the TME, tumor regions, and stroma regions exhibited inconsistency. The proportions of CD68+ SHP2+ TAMs (P < 0.05) were higher in tumor than in stroma. And the high infiltration of CD68+SHP2+ TAMs in tumor areas correlated with poor OS (P < 0.05). We found that the expression level of SHP2 was higher in M2-like macrophages than in M1-like macrophages. The CD68+SHP2+ subset proportion was positively correlated with the CD68+CD206+ subset within TME (P < 0.0001), tumor (P < 0.0001) and stroma (P < 0.0001). Conclusions: The high infiltration of CD68+SHP2+ TAMs predict poor OS in NSCLC. Targeting SHP2 is a potentially effective strategy to inhibit M2-phenotype polarization. And it provides a new thought for SHP2 targeted cancer immunotherapy.

Spatial ecology and microhabitat selection of the nocturnal pitviper Viridovipera stejnegeri (Squamata: Viperidae) in relation to prey.

Habitat is fundamental for facilitating various life activities in animals, for instance, snakes procure essential energy for survival and reproduction by selecting ambush microhabitats. While there has been extensive research on the selection of microhabitat for feeding in terrestrial and aquatic snakes, little is known about arboreal snakes. In the present study, we analyzed the ambush microhabitat preferences of Viridovipera stejnegeri, a widely distributed Asian pitviper in China, conducted association analysis between snake microhabitat and prey microhabitat and abundance to determine the ro5le of microhabitat selection in feeding. Employing random forest analysis and habitat selection functions, we further constructed a predictive framework for assessing the probability of ambush site selection by V. stejnegeri. Our results revealed that V. stejnegeri exhibited a distinct microhabitat preference for ambush prey. Among the 13 environmental factors assessed, V. stejnegeri showed pronounced preferences towards 12 of these factors, including climatic factors, geographical factors, and vegetation factors. Furthermore, although the preferences of V. stejnegeri overlapped substantially with those of its prey across multiple habitat factors, food abundance shows no significant association with various habitat factors of V. stejnegeri, and does not have significant predictive effect on habitat selection of V. stejnegeri. Therefore, we infer that V. stejnegeri does not preferentially select microhabitats with the highest food abundance, which does not support the hypothesis that "snakes select habitats based on the spatial distribution of prey abundance." By analyzing the characteristics of vegetation, geography, and climate, we conclude that V. stejnegeri tends to choose microhabitats with better ambush conditions to increase attack success rate, thereby achieving the optimal feeding success rate at the microhabitat scale, which is in line with the predictions of optimal foraging theory. This study provides new insights into the predation ecology and habitat selection of snakes.

Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network.

BACKGROUND: Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. RESULTS: This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. CONCLUSION: The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.

Analysis of coupled coordination and spatial interaction effects between manufacturing and logistics industries in the Yellow River Basin of China.

The coupled and coordinated development of the manufacturing and logistics industries has become an inevitable choice for achieving high-quality development in both sectors. In this study, we focused on nine provinces located in the Yellow River Basin and analyzed panel data from 2010 to 2021. Our analysis, based on the super-efficient SBM-undesirable model, revealed that the coupling and coordination efficiency between the two industries in the region is moderate, with significant regional disparities. Additionally, using the Global Moran's I and the local Moran's I, we tested the spatial autocorrelation of the two industries and analyzed their spatial interaction effect using SDM. The study reveals that the manufacturing and logistics industries in the Yellow River Basin exhibit moderate coupling and coordination efficiency with significant regional variations. We found that the logistics industry plays a more supportive role in the manufacturing industry, particularly in Henan and Shandong provinces. Spatial spillover effects in terms of informationization, openness to the outside world, and energy consumption are more significant, while infrastructure investment does not exhibit significant spatial interaction effects. Based on our findings, we propose relevant development strategies for the two industries.

Haze pollution and urbanization promotion in China: How to understand their spatial interaction?

Can promoting urbanization and controlling haze pollution result in a win-win situation? Based on panel data from 287 prefecture-level cities in China, this paper uses the three-stage least-squares estimator method(3SLS) and generalized space three-stage least-squares estimator method (GS3SLS) to study the spatial interaction between haze pollution and urbanization. The results show the following: (1) There is a spatial interaction between haze pollution and urbanization. On the whole, haze pollution and urbanization have a typical inverted U-shaped relationship. (2) Haze and urbanization show different relationships in different regions. The haze pollution in the area left of the Hu Line has a linear relationship with urbanization. (3) In addition to haze, urbanization also has a spatial spillover effect. When the haze pollution in the surrounding areas increases, the haze pollution in the area will also increase, but the level of urbanization will increase. When the level of urbanization in the surrounding areas increases, it will promote the level of urbanization in the local area and alleviate the haze pollution in the local area. (4) Tertiary industry, greening, FDI and precipitation can help alleviate haze pollution. FDI and the level of urbanization have a U-shaped relationship. In addition, industry, transportation, population density, economic level and market scale can promote regional urbanization.

Artificial intelligence-quantified tumour-lymphocyte spatial interaction predicts disease-free survival in resected lung adenocarcinoma: A graph-based, multicentre study.

BACKGROUND AND OBJECTIVE: A high degree of lymphocyte infiltration is related to superior outcomes amongst patients with lung adenocarcinoma. Recent evidence indicates that the spatial interactions between tumours and lymphocytes also influence the anti-tumour immune responses, but the spatial analysis at the cellular level remains insufficient. METHODS: We proposed an artificial intelligence-quantified Tumour-Lymphocyte Spatial Interaction score (TLSI-score) by calculating the ratio between the number of spatial adjacent tumour-lymphocyte and the number of tumour cells based on topology cell graph constructed using H&E-stained whole-slide images. The association of TLSI-score with disease-free survival (DFS) was explored in 529 patients with lung adenocarcinoma across three independent cohorts (D1, 275; V1, 139; V2, 115). RESULTS: After adjusting for pTNM stage and other clinicopathologic risk factors, a higher TLSI-score was independently associated with longer DFS than a low TLSI-score in the three cohorts [D1, adjusted hazard ratio (HR), 0.674; 95% confidence interval (CI) 0.463-0.983; p = 0.040; V1, adjusted HR, 0.408; 95% CI 0.223-0.746; p = 0.004; V2, adjusted HR, 0.294; 95% CI 0.130-0.666; p = 0.003]. By integrating the TLSI-score with clinicopathologic risk factors, the integrated model (full model) improves the prediction of DFS in three independent cohorts (C-index, D1, 0.716 vs. 0.701; V1, 0.666 vs. 0.645; V2, 0.708 vs. 0.662) CONCLUSIONS: TLSI-score shows the second highest relative contribution to the prognostic prediction model, next to the pTNM stage. TLSI-score can assist in the characterising of tumour microenvironment and is expected to promote individualized treatment and follow-up decision-making in clinical practice.

Does industrial agglomeration and environmental pollution have a spatial spillover effect?: taking panel data of resource-based cities in China as an example.

With the continous advancement of industrialization, the environmental problems among resource-based cities in China are becoming increasingly serious. Therefore, exploring the spatiotemporal evolution characteristics of industrial agglomeration and environmental pollution to understand the interaction mechanism between them is of great significance for the coordinated development of resource-based cities. In this paper, 114 resource-based cities in China are selected as research objects, and data from the period of 2011 to 2020 are used for analysis and research. Location entropy and entropy weight models are used to measure the relevant indicators, and a spatial simultaneous equation model is constructed. The generalized spatial three-stage least squares (GS3SLS) method is used to assess the spatial interactive spillover effects of industrial agglomeration on environmental pollution. The research results show that ① the industrial agglomeration center of China has begun to shift to the junction of Shanxi, Shaanxi, and Inner Mongolia, and the level of environmental pollution has dropped significantly; ② industrial agglomeration promotes environmental pollution, while environmental pollution inhibits industrial agglomeration, and between them, the inhibition effect is more obvious; ③ the level of industrial agglomeration in surrounding cities promotes industrial agglomeration in the region and inhibits environmental pollution, while the level of environmental pollution in surrounding cities inhibits industrial agglomeration in the region and promotes the diffusion of pollution. Thus, the building of a coordinated development system across regions can achieve a win-win situation between industrial development and environmental protection.

How does green credit reduce carbon emissions? Dynamic spatial interactions and regional disparities.

Green credit is an important green financial policy tool to promote green development. However, research is needed to explore how green credit reduces carbon emissions, especially with respect to its dynamic spatial interactions and regional disparities. Based on a theoretical analysis, this paper empirically tests the carbon emission reduction effect of green credit and its three mechanisms by combining a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, dynamic spatial Durbin model (SDM), and the mediation model, including their dynamic spatial interactions and regional disparities. The study concludes that green credit can reduce carbon emission intensity based on strong spatio-temporal interactions in China. Green credit mainly reduces carbon emission intensity through scale and technology mechanisms with different spatio-temporal interactions. The tertiary industry in China does not currently have completely clean production; as such, the upgrading of the industrial structure as stimulated by green credit in the long term cannot yet effectively reduce carbon emissions. In addition, the carbon emission reduction effect of green credit and its three mechanisms have different levels of performance and dynamic spatial interactions in different regions of China. Finally, targeted policy recommendations are proposed to apply green credit to effectively reduce the carbon emission intensity.

The role of spatial interplay patterns between PD-L1-positive tumor cell and T cell in recurrence of locally advanced non-small cell lung cancer.

PURPOSE: To explore the relationship between the spatial interaction of programmed death-ligand 1(PD-L1)-positive tumor cell and T cell with specific functions and the recurrence of non-small cell lung cancer (NSCLC) and optimize prognostic stratification. MATERIALS AND METHODS: This study retrospectively included 104 patients with locally advanced NSCLC who underwent radical surgery. Tissue microarrays were constructed including tumor center (TC) and invasion margin (IM), and CK/CD4/CD8/PD-L1/programmed death-1 (PD-1) was labeled using multiplex immunofluorescence to decipher the counts and spatial distribution of tumor cells and T cells. The immune microenvironment and recurrence stratification were characterized using the Mann-Whitney U test and Cox proportional hazards model. RESULT: Compared with the IM, the proportion of tumor cells (especially PD-L1+) was increased in the TC, while T cells (especially PD-1+) were decreased. An increase in TC PD-1+ CD8 T cells promoted relapse (HR = 2.183), while PD-L1+ tumor cells alone or in combination with T cells had no predictive value for relapse. In addition, in both TC and IM, CD8 were on average closer to PD-L1+ tumor cells than CD4, especially exhausted CD8. The effective density and percentage of PD-1+ CD4 T cells interacting with PD-L1+ tumor cells in the IM were both associated with recurrence, and the HRs increased sequentially (HRs were 2.809 and 4.063, respectively). Patients with low PD-1+CD4 count combined high PD-1+CD4 effective density showed significantly poorer RFS compared to those with high PD-1+CD4 count combined low PD-1+CD4 effective density, in both the TC and IM regions (HRs were 5.810 and 8.709, respectively). CONCLUSION: Assessing the relative spatial proximity of PD-1/PD-L1 contributes to a deeper understanding of tumor immune escape and generates prognostic information in locally advanced NSCLC patients.

Assessing Spatial Accessibility of Community Hospitals for the Elderly in Beijing, China.

Accessibility of health services signifies the quality and equitability of universal health provision. The hierarchical medical system recently implemented in China offers the policy instruments to improve medical services to the elderly in an aging society. As the critical primary care gateway, accessibility to community hospitals has significant impacts on people's health. However, current research has paid little attention to spatial accessibility within walking distance of community hospitals, especially for the elderly. This study selected four districts with different urbanization levels in the rapidly developing Beijing metropolis. The spatial interaction model was applied to measure the accessibility of community hospitals for the elderly at the community level. An attractiveness index was computed based on key hospital traits. The results showed that: (1) community hospitals could cover 82.66% of elderly residents, and 77.63% of the communities were within walking distance. The served elderly proportion was relatively high in central urban areas and low in the suburbs. (2) The attractiveness indices of hospitals varied notably between districts, with higher values in more urbanized areas. (3) The spatial accessibility for the elderly of hospitals differed significantly between the four districts, with a descending gradient from central to suburban and rural areas, as indicated by the Gini coefficients and Lorenz curves. (4) The accessibility index was strongly related to the served elderly population and the hospital-residence distance. The findings provide policy directions to the government, including providing more primary-care resources to suburban and rural areas, building new community hospitals in identified provision gaps, upgrading some clinics to hospitals in rural areas, and planning hospitals according to the projected trend of the elderly population in terms of quantity and distribution. The considerable provision disparity between core urban, suburban and rural areas can be addressed by refined spatial health planning informed by research.

Effects of municipal boundaries measured by combining urban scaling and spatial interaction.

Urban scaling, the superlinear increase of socio-economic measures with increasing population, is a well-researched phenomenon. This article is focused on socio-economic performance scaling, which could possibly be driven by increasing returns of the size and density of interaction networks. If this is indeed the case, we should also find that spatial barriers to interaction affect scaling and cause local performance deviations. Possible barring effects of municipal boundaries are important from the perspective of urban and regional governance. We test the hypothesis of barring effects by correlating municipal boundaries with the structure of commuter networks within a large densely urbanized region, the Randstad in The Netherlands. The measured impacts of these boundaries are correlated with local employment-scaling deviations. Applying spatially weighted modelling techniques, we find that municipal borders have significant effects on inter-municipal commuting and indicate these effects on the map. The results show particularly significant correlations along dividing lines between large urban agglomerations and rural communities. The southern part of the Randstad is more fragmented by such dividing lines than the northern part, which could partly explain the diverging economic development between the two parts.

Modeling and migration-based control of depopulation.

This study deals with the problem of the population shrinking in habitats affected by aging and excessive migration outflows. First, a control-oriented population dynamics model was proposed that catches the effect of depopulation. The model also includes the effect of spatial interaction-driven migration flows on population size. The resulting model is a non-homogeneous ordinary differential equation. It includes such phenomena that are important from the control point of view, such as the influence of migration costs on population dynamics, the impact of aging on population size, or the effect of the habitats' carrying capacity on migration flows. Based on the model, controllability conditions are formulated and a control strategy was developed that is meant to avoid the depopulation of the habitat. The control method acts on the migration costs to achieve the control goal and requires only population size measurements. Simulation measurements are presented in the paper to show the effectiveness of the proposed modeling and control methods.

Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information.

BACKGROUND: Cell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools. RESULTS: We benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods. CONCLUSIONS: Our study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub ( https://github.com/wanglabtongji/CCI ).

Indigenous communities of Peru: Level of accessibility to health facilities.

Objectives: This study aimed to geospatially model the level of geographic accessibility to health facilities among Amazonian Indigenous communities in a region of Peru. Methods: Spatial modeling of the physical accessibility of the Indigenous communities to the nearest health facility was performed through cost-distance analysis. The study area was Loreto, the region with the largest territorial area and number of Indigenous communities in Peru. The time required to reach a health facility was determined by cumulatively adding the time needed to cross the grids on the lowest cost route from the Indigenous communities' locations to the nearest health facility, by considering Amazonian geographical conditions and the main types of transport used. Results: The median time to reach a health facility was 0.96 h (interquartile range: 0.45-2.41). Of the total communities (n = 1043), only 479 (45.93%) communities were within 1 h from the nearest health facility, and 161 (15.44%) were more than 8 h away. The Indigenous communities more than 8 h away from a health establishment were located in the border areas of the department of Loreto. Conclusion: One in two Indigenous communities is more than 1 h from the nearest health facility.

The Spatial Interaction Effect of Green Spaces on Urban Economic Growth: Empirical Evidence from China.

This paper measures the impact of urban green space construction rate on urban economic growth from the perspective of spatial interaction. To this end, we collect panel data of 31 provincial capital cities in China from 2001 to 2020 and use spatial economics models for empirical testing. The research results are summarized as follows: the level of green space construction can attract talents and investment by improving the environmental level of the city, and these financial expenditures, foreign investment, and talents are conducive to urbanization, thus having a significant positive impact on urban economic development. In addition, it also has a significant positive spatial spillover effect. In addition, the construction of urban green space will also stimulate the environmental protection of neighboring cities, which has a significant positive spatial dependence. At this time, talents and investment are affected by the environmental construction of neighboring cities, and the economic development of the city has also been significantly improved. The spatial spillover effect of green space construction on the economic level of surrounding cities is also positive. The empirical conclusions provide references for implementing green development strategies and promoting high-quality economic development.

Geospatial analysis of cesarean section in Iran (2016-2020): exploring clustered patterns and measuring spatial interactions of available health services.

BACKGROUND: The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spatiotemporal variations of CS rates in each geographical area could provide helpful information to understand the status of using CS services. METHODS: This cross-sectional study explored spatiotemporal patterns of CS in northeast Iran from 2016 to 2020. Space-time scan statistics and spatial interaction analysis were conducted using geographical information systems to visualize and explore patterns of CS services. RESULTS: The temporal analysis identified 2017 and 2018 as the statistically significant high clustered times in terms of CS rate. Five purely spatial clusters were identified that were distributed heterogeneously in the study region and included 14 counties. The spatiotemporal analysis identified four clusters that included 13 counties as high-rate areas in different periods. According to spatial interaction analysis, there was a solid spatial concentration of hospital facilities in the political center of the study area. Moreover, a high degree of inequity was observed in spatial accessibility to CS hospitals in the study area. CONCLUSIONS: CS Spatiotemporal clusters in the study area reveal that CS use in different counties among women of childbearing age is significantly different in terms of location and time. This difference might be studied in future research to identify any overutilization of CS or lack of appropriate CS in clustered counties, as both put women at risk. Hospital capacity and distance from population centers to hospitals might play an essential role in CS rate variations and spatial interactions among people and CS facilities. As a result, some healthcare strategies, e.g., building new hospitals and empowering the existing local hospitals to perform CS in areas out of service, might be developed to decline spatial inequity.

Spatial differentiation and determinants of COVID-19 in Indonesia.

BACKGROUND: The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors. METHODS: The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020. RESULTS: Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali. CONCLUSION: Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.

Cells-Micropatterning Biomaterials for Immune Activation and Bone Regeneration.

Natural tissues are composed of ordered architectural organizations of multiple tissue cells. The spatial distribution of cells is crucial for directing cellular behavior and maintaining tissue homeostasis and function. Herein, an artificial bone bioceramic scaffold with star-, Tai Chi-, or interlacing-shaped multicellular patterns is constructed. The "cross-talk" between mesenchymal stem cells (MSCs) and macrophages can be effectively manipulated by altering the spatial distribution of two kinds of cells in the scaffolds, thus achieving controllable modulation of the scaffold-mediated osteo-immune responses. Compared with other multicellular patterns, the Tai Chi pattern with a 2:1 ratio of MSCs to macrophages is more effective in activating anti-inflammatory M2 macrophages, improving MSCs osteogenic differentiation, and accelerating new bone formation in vivo. In brief, the Tai Chi pattern generates a more favorable osteo-immune environment for bone regeneration, exhibiting enhanced immunomodulation and osteogenesis, which may be associated with the activation of BMP-Smad, Oncostatin M (OSM), and Wnt/ß-catenin signaling pathways in MSCs mediated by macrophage-derived paracrine signaling mediators. The study suggests that the manipulation of cell distribution to improve tissue formation is a feasible approach that can offer new insights for the design of tissue-engineered bone substitutes with multicellular interactions.

Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement.

BACKGROUND: Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. METHODS: We modeled infection risk from seating and fine-grained movement patterns - boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. RESULTS: Our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95-100%, while cloth masks would have reduced it by only 40-80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. CONCLUSIONS: Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.

Functional Similarities of Protein-Coding Genes in Topologically Associating Domains and Spatially-Proximate Genomic Regions.

Topologically associating domains (TADs) are the structural and functional units of the genome. However, the functions of protein-coding genes existing in the same or different TADs have not been fully investigated. We compared the functional similarities of protein-coding genes existing in the same TAD and between different TADs, and also in the same gap region (the region between two consecutive TADs) and between different gap regions. We found that the protein-coding genes from the same TAD or gap region are more likely to share similar protein functions, and this trend is more obvious with TADs than the gap regions. We further created two types of gene-gene spatial interaction networks: the first type is based on Hi-C contacts, whereas the second type is based on both Hi-C contacts and the relationship of being in the same TAD. A graph auto-encoder was applied to learn the network topology, reconstruct the two types of networks, and predict the functions of the central genes/nodes based on the functions of the neighboring genes/nodes. It was found that better performance was achieved with the second type of network. Furthermore, we detected long-range spatially-interactive regions based on Hi-C contacts and calculated the functional similarities of the gene pairs from these regions.