Statistical Inference Using GLEaM Model with Spatial Heterogeneity and Correlation between Regions

A better understanding of the various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the celebrated GLEaM model (Balcan et al., 2010 [1]), this paper proposes a stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.

Spatial and Temporal Heterogeneity of Tumor-Infiltrating Lymphocytes in Advanced Urothelial Cancer

Checkpoint inhibitors targeting PD-(L)1 induce objective responses in 20% of patients with metastatic urothelial cancer (UC). CD8+ T cell infiltration has been proposed as a putative biomarker for response to checkpoint inhibitors. Nevertheless, data on spatial and temporal heterogeneity of tumor-infiltrating lymphocytes in advanced UC are lacking. The major aims of this study were to explore spatial heterogeneity for lymphocyte infiltration and to investigate how the immune landscape changes during the disease course. We performed multiplex immunohistochemistry to assess the density of intratumoral and stromal CD3+, CD8+, FoxP3+ and CD20+ immune cells in longitudinally collected samples of 49 UC patients. Within these samples, spatial heterogeneity for lymphocyte infiltration was observed. Regions the size of a 0.6 tissue microarray core (0.28 mm2) provided a representative sample in 60.6 to 71.6% of cases, depending on the cell type of interest. Regions of 3.30 mm2, the median tumor surface area in our biopsies, were representative in 58.8 to 73.8% of cases. Immune cell densities did not significantly differ between untreated primary tumors and metachronous distant metastases. Interestingly, CD3+, CD8+ and FoxP3+ T cell densities decreased during chemotherapy in two small cohorts of patients treated with neoadjuvant or palliative platinum-based chemotherapy. In conclusion, spatial heterogeneity in advanced UC challenges the use of immune cell infiltration in biopsies as biomarker for response prediction. Our data also suggests a decrease in tumor-infiltrating T cells during platinum-based chemotherapy.

Progress and Prospects of Forest Ecological Asset Research

In recent years, in the face of the deterioration of the ecological environment, the research on forest ecological assets (FEA) has increasingly become a focal area of ecological research. To understand the current research progress and future prospects, this paper classifies and summarizes the main progress and achievements related to FEA in terms of theoretical studies, index systems, technical methods, and accounting models. In view of the existing research results, this paper proposes seven key scientific and technical problems and prospects to be solved, including the unification of the concept of ecological assets, the focus of future research on FEA, the construction of an evaluation index system according to local conditions, the integration of data assimilation methods that complement ground and remote sensing observations, the study of the spatial and temporal heterogeneity of forest ecological assets, the study of the net value of FEA, and the preservation and appreciation of FEA.

Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations

Recently, remotely sensed measurements of the near-infrared reflectance (NIRv) of vegetation, the fluorescence correction vegetation index (FCVI), and radiance (NIRvrad) of vegetation have emerged as indicators of vegetation structure and function with potential to enhance or improve upon commonly used indicators, such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). The applicability of these remotely sensed indices to tropical forests, key ecosystems for global carbon cycling and biodiversity, has been limited. In particular, fine-scale spatial and temporal heterogeneity of structure and physiology may contribute to variation in these indices and the properties that are presumed to be tracked by them, such as gross primary productivity (GPP) and absorbed photosynthetically active radiation (APAR). In this study, fine-scale (approx. 15 cm) tropical forest heterogeneity represented by NIRv, FCVI, and NIRvrad and by lidar-derived height is investigated and compared to NIRv and EVI using unoccupied aerial system (UAS)-based hyperspectral and lidar sensors. By exploiting near-infrared signals, NIRv, FCVI, and NIRvrad captured the greatest spatiotemporal variability, followed by the enhanced vegetation index (EVI) and then the normalized difference vegetation index (NDVI). Wavelet analyses showed the dominant spatial scale of variability of all indicators was driven by tree clusters and larger-than-tree-crown size gaps rather than individual tree crowns. NIRv, FCVI, NIRvrad, and EVI captured variability at smaller spatial scales (∼ 50 m) than NDVI (∼ 90 m) and the lidar-based surface model (∼ 70 m). We show that spatial and temporal patterns of NIRv and FCVI were virtually identical for a dense green canopy, confirming predictions in earlier studies. Furthermore, we show that NIRvrad, which does not require separate irradiance measurements, correlated more strongly with GPP and PAR than did other indicators. NIRv, FCVI, and NIRvrad, which are related to canopy structure and the radiation regime of vegetation canopies, are promising tools to improve understanding of tropical forest canopy structure and function.

Assessing population exposure to coastal flooding due to sea level rise

The exposure of populations to sea-level rise (SLR) is a leading indicator assessing the impact of future climate change on coastal regions. SLR exposes coastal populations to a spectrum of impacts with broad spatial and temporal heterogeneity, but exposure assessments often narrowly define the spatial zone of flooding. Here we show how choice of zone results in differential exposure estimates across space and time. Further, we apply a spatio-temporal flood-modeling approach that integrates across these spatial zones to assess the annual probability of population exposure. We apply our model to the coastal United States to demonstrate a more robust assessment of population exposure to flooding from SLR in any given year. Our results suggest that more explicit decisions regarding spatial zone (and associated temporal implication) will improve adaptation planning and policies by indicating the relative chance and magnitude of coastal populations to be affected by future SLR.

Spatial and temporal intratumour heterogeneity has potential consequences for single biopsy-based neuroblastoma treatment decisions

Intratumour heterogeneity is a major cause of treatment failure in cancer. We present in-depth analyses combining transcriptomic and genomic profiling with ultra-deep targeted sequencing of multiregional biopsies in 10 patients with neuroblastoma, a devastating childhood tumour. We observe high spatial and temporal heterogeneity in somatic mutations and somatic copy-number alterations which are reflected on the transcriptomic level. Mutations in some druggable target genes including ALK and FGFR1 are heterogeneous at diagnosis and/or relapse, raising the issue whether current target prioritization and molecular risk stratification procedures in single biopsies are sufficiently reliable for therapy decisions. The genetic heterogeneity in gene mutations and chromosome aberrations observed in deep analyses from patient courses suggest clonal evolution before treatment and under treatment pressure, and support early emergence of metastatic clones and ongoing chromosomal instability during disease evolution. We report continuous clonal evolution on mutational and copy number levels in neuroblastoma, and detail its implications for therapy selection, risk stratification and therapy resistance.

Identification of Dominant Runoff Controls Using Hydrologically Informed Machine Learning Approach

Relative dominance of the runoff controls, such as topography, geology, soil types, land use, and climate, may differ from catchment to catchment due to spatial and temporal heterogeneity of landscape properties and climate variables. Understanding dominant runoff controls is an essential task in developing unified hydrological theories at the catchment scale. Semi-distributed rainfall-runoff models are often used to identify dominant runoff controls for a catchment of interest. In most such applications, the model selection is based on either expert's judgement or experimental and fieldwork insights. Model selection is the most important step in any hydrological modelling exercise as the findings are largely influenced by the selected model. Hence, a subjective model selection without sufficient expert's knowledge or experimental insights may result in biased findings, especially for comparative studies like identification of dominant runoff controls. In this study, we use a physics informed machine learning toolbox based on genetic programming Machine Induction Knowledge Augmented - System Hydrologique Asiatique (MIKA-SHA) to identify the relative dominance of runoff controls. We find the quantitative and automated approach based on MIKA-SHA to be highly appropriate for the intended task. MIKA-SHA does not require explicit user selections and relies on data and fundamental hydrological processes. The approach is tested using the Rappahannock River basin at Remington, Virginia, United States. Two rainfall-runoff models are learnt to represent the runoff dynamics of the catchment using topography-based and soil-type-based hydrologic response units independently. Based on prediction capabilities, in this case, the topography is identified as the dominant runoff driver.

The Influence of Strip-City Street Network Structure on Spatial Vitality: Case Studies in Lanzhou, China

In the context of China’s recent urbanization, the agglomeration and diffusion of the strip-city spatial network are gradually being reconstructed. The ways in which the street network structure affects the underlying logic of economic and social development is worthy of in-depth consideration. This study takes Lanzhou (a typical strip city in China) as a case study, using dynamic, geographic, big data and spatial syntactic-theory models to explore the influence of street network accessibility and structure on the spatial and temporal distribution of strip-city spatial vitality. We use Hotspot Analysis (Getis-Ord Gi*) to analyze the dispersal characteristics of street space vitality. In addition, the spatial and temporal heterogeneity characteristics and mechanism of the influence of street accessibility on spatial vitality are evaluated using the spatial Durbin model (SDM). The results show that: the temporal and spatial performance of urban vitality on weekdays and weekends conforms to people’s daily activities, offering similar spatial agglomeration and dispersion effects; accessibility and pedestrian-friendly streets have better urban spatial vitality clustering; street network integration significantly affects the reshaping of urban vitality, but there is apparent temporal heterogeneity in the degree of impact.

Parameter estimation and hypothesis testing of geographically and temporally weighted bivariate generalized Poisson regression

Poisson regression is used to model the data with the response variable in the form of count data. This modeling must meet the equidispersion assumption. That is, the average value is the same as the variance. However, this assumption is often violated. Violation of the equidispersion assumption in Poisson regression modeling will result in invalid conclusions. These violations are an overdispersion and an underdispersion of the response variable. Generalized Poisson Regression (GPR) is an alternative if there is a violation of the equidispersion assumption. If there are two correlated response variables, modeling will use the Bivariate Generalized Poisson Regression (BGPR). However, in the panel data with the observation unit in the form of an area, BGPR is not quite right because there is spatial and temporal heterogeneity in the data. Geographically and Temporally Weighted Bivariate Generalized Poisson Regression (GTWBGPR) is a method for modeling spatial and temporal heterogeneity data. GTWBGPR is a development of GWBGPR. In GTWBGPR, besides accommodating spatial effects, it also accommodates temporal effects. This research will discuss the parameter estimation and test statistics for the GTWBGPR model. Parameter estimation uses Maximum Likelihood Estimation (MLE), but the result is not closed-form, so it is solved by numerical iteration. The numerical iteration used is Newton-Raphson. The test statistic for simultaneous testing uses the Maximum Likelihood Ratio Test (MLRT). With large samples, then this test statistic has a chi-square distribution approximation. So the test statistic for the partial test uses the Z test statistic.