Time is of the essence: Exploring temporal and spatial organisation in episodic memory

There is disagreement in the literature as to whether episodic memory maintains an inherent temporal organisation, that is, whether learned items are necessarily organised along some temporal dimension or whether temporal organisation is a task-specific occurrence. The current series of experiments explored this issue. In Experiment 1, we tested whether temporal or spatial contiguity was present in an incidental encoding task where either strategy (but not both together) could be employed at test. In Experiment 2, we attempted to facilitate the use of a spatial retrieval strategy at test by asking participants to recall the location where target items had been displayed at study, after incidental encoding. Experiment 3 explored the role of study-test congruency by informing participants at encoding that they would be tested on either their memory for the temporal sequence or spatial locations, and then testing both at retrieval. Finally, Experiment 4 employed a masking task at encoding to ensure participants could not predict the true nature of the task, despite it being incidental, and a surprise free recall task. Predominantly, participants displayed recall performance consistent with temporal contiguity, although there was evidence for spatial contiguity under certain conditions. These results are consistent with the notion that episodic memory has a stable and predictable temporal organisation.

Associative recognition memory for identity, spatial and temporal relations in healthy aging

The ability to generate associative representations and to retrieve them from long-term episodic memory generally declines in healthy aging. However, it is unclear whether healthy aging has differential effects on associative memory for identity, spatial configuration, and temporal order relationships. In the current study, we assessed how healthy aging impacts on associative memory for identity, spatial, or temporal relationships between pairs of visual objects via discrimination of intact and rearranged pairs. Accuracy and response time performance of healthy older adults (aged 65-80) were compared with young adults (ages 19-30). Age-related declines in associative memory were observed equally for all types of associations, but these declines differed by associative status: aging most strongly affected ability to discriminate rearranged pairs. These results suggest that associative memory for identity, spatial, and temporal relationships are equally affected by healthy aging, and may all depend on a shared set of basic associative mechanisms.

Spatial analysis and risk factors of suicide among people living with HIV/AIDS who committed suicide

Poor mental health may confer worse health outcomes among people living with HIV/AIDS (PLWH). Suicidal behaviors are symptoms of a depressive episode. The goal was to explore characteristics for spatial distribution and risk factors for suicide among PLWH. This study was conducted in Mainland China with the annual newly reported data of PLWH from 2013 to 2018. We compared the spatial distribution differences between the HIV epidemic and suicide mortality and analyzed the global and local spatial analysis of suicide. Further, we explored the possible risk factors of suicide in PLWH by multivariate regression and a decision tree model. High suicide mortality regions in PLWH in China were inconsistent with that of the high prevalence of PLWH, which showed that there was distribution discordance between the HIV epidemic and suicide mortality. Multivariate regression showed that the possible risk factors of PLWH who committed suicide were younger age, with Han nationality, single, having a higher educational level, and homosexual infection route. The decision tree model showed that age was the primary factor. In conclusion, there was a discordance between HIV prevalence and suicide mortality; lower HIV prevalence regions might have higher mortality due to suicide. Concern is merited amongst PLWH with poor mental health.

Predicting and Interpreting Spatial Accidents through MDLSTM

Predicting and interpreting the spatial location and causes of traffic accidents is one of the current hot topics in traffic safety. This research purposed a multi-dimensional long-short term memory neural network model (MDLSTM) to fit the non-linear relationships between traffic accident characteristics and land use properties, which are further interpreted to form local and general rules. More variables are taken into account as the input land use properties and the output traffic accident characteristics. Five types of traffic accident characteristics are simultaneously predicted with higher accuracy, and three levels of interpretation, including the hidden factor-traffic potential, the potential-determine factors, which varies between grid cells, and the general rules across the whole study area are analyzed. Based on the model, some interesting insights were revealed including the division line in the potential traffic accidents in Shenyang (China). It is also purposed that the relationship between land use and accidents differ from previous researches in the neighboring and regional aspects. Neighboring grids have strong spatial connections so that the relationship of accidents in a continuous area is relatively similar. In a larger region, the spatial location is found to have a great influence on the traffic accident and has a strong directionality.

Individual and Community Social Determinants of Health Associated With Diabetes Management in a Mexican American Population

Background: Diabetes is a major health burden in Mexican American populations, especially among those in the Lower Rio Grande Valley (LRGV) in the border region of Texas. Understanding the roles that social determinants of health (SDOH) play in diabetes management programs, both at the individual and community level, may inform future intervention strategies.

Methods: This study performed a secondary data analysis on 1,568 individuals who participated in Salud y Vida (SyV), a local diabetes and chronic disease management program, between October 2013 and September 2018 recruited from a local clinic. The primary outcome was the reduction of hemoglobin A1C (HbA1C) at the last follow-up visit compared to the baseline. In addition to age, gender, insurance status, education level and marital status, we also investigated 15 community (census tract) SDOH using the American Community Survey. Because of the high correlation in the community SDOH, we developed the community-level indices representing different domains. Using Bayesian multilevel spatial models that account for the geographic dependency, we were able to simultaneously investigate the individual- and community-level SDOH that may impact HbA1C reduction.

Results: After accounting for the diabetes self-management education classes taken by the participants and their length of stay in the program, we found that older age at baseline, being married (compared to being widowed or divorced) and English speaking (compared to Spanish) were significantly associated with greater HbA1C reduction. Moreover, we found that the community level SDOH were also highly associated with HbA1C reduction. With every percentile rank decrease in the socioeconomic advantage index, we estimated an additional 0.018% reduction in HbA1C [95% CI (−0.028, −0.007)]. Besides the socioeconomic advantage index, urban core opportunity and immigrant's cohesion and accessibility indices were also statistically associated with HbA1C reduction.

Conclusion: To our knowledge, our study is the first to utilize Bayesian multilevel spatial models and simultaneously investigate both individual- and community-level SDOH in the context of diabetes management. Our findings suggest that community SDOH play an important role in diabetes control and management, and the need to consider community and neighborhood context in future interventions programs to maximize their overall effectiveness.

Spatially resolved transcriptomics and its applications in cancer

Spatially resolved transcriptomics (SRT) offers the promise of understanding cells and their modes of dysfunction in the context of intact tissues. Technologies for SRT have advanced rapidly with a large number being published in recent years. Diverse methods for SRT produce data at widely varying depth, throughput, accessibility and cost. Many published SRT methods have been demonstrated only in their labs of origin, while others have matured to the point of commercialization and widespread availability. Here we review technologies for SRT, and their application in studies of tumor heterogeneity.

HIV influences clustering and intracellular replication of hepatitis C virus

HCV and HIV coinfection is common and HIV leads to increased HCV viraemia and accelerated disease progression. However, the biological basis of this interaction remains poorly understood and little is known about the impact of HIV on HCV replication at the cellular level. We analysed HCV RNA, based on single-cell laser-capture microdissection, in liver biopsies from monoinfected (n = 4) and HCV/HIV-coinfected (n = 5) participants. HCV RNA was assayed in 3200 hepatocytes with information of spatial position. We compared HCV RNA levels and clustering properties of infection between mono- and coinfected participants, and developed a mathematical model of infection. Although the median plasma HCV RNA level and the fraction of infected cells were comparable in monoinfected (7.0 log₁₀ IU/mL and ~ 30%) and coinfected (7.3 log₁₀ IU/mL and ~ 40%) participants, the median HCV RNA per infected hepatocyte in monoinfected (2.8IU) was significantly lower than in coinfected (8.2IU) participants (p = .03). Clustering of infected cells was more prominent in monoinfected participants (91% of samples) than in coinfected participants (~48%), p = .0045, suggesting that spatial spread may be influenced by HIV coinfection. Interestingly, when clustering does occur, the size of clusters is similar in both types of infection. A mathematical model of infection suggested that HIV allows higher intracellular accumulation of HCV RNA by impeding the export of HCV RNA. Our observations show that HIV coinfection impacts intracellular accumulation of HCV RNA and the clustering of HCV-infected cells, but to a less extent the fraction of HCV-infected cells.

Association between access to convenience stores and childhood obesity: A systematic review

Childhood obesity increases the risk of adulthood obesity and is associated with other adverse health outcomes later in life. It may be influenced by environmental characteristics of neighborhoods where children live, particularly dietary supply-related environmental factors. This study aimed to systematically review the evidence on the association between access to convenience stores and childhood obesity. We searched and filtered relevant literature in PubMed, Embase, Web of Science, and Cochrane Library published before 1 January 2019. Data on the basic characteristics of studies, measures of access to convenience stores, and associations of convenience stores with weight-related behaviors and outcomes were extracted from 41 included studies. In general, the density of and proximity to convenience stores in children's residential and school neighborhoods were positively associated with unhealthy eating behaviors. However, their associations with children's weight status varied significantly by regions. The association between convenience store access and children's weight status was found to be negative in Canada, rather mixed in the United States and the United Kingdom, and not significant in East Asia. We suggest future research to clearly define the convenience store, better measure the access to convenience store, and also measure children's journey and food purchasing and consumption behaviors, to explain pathways from convenience store access to childhood obesity for designing effective interventions and policies.

Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis

Dengue is an important emerging vector-borne disease in Bhutan. This study aimed to quantify the spatial and temporal patterns of dengue and their relationship to environmental factors in dengue-affected areas at the sub-district level. A multivariate zero-inflated Poisson regression model was developed using a Bayesian framework with spatial and spatiotemporal random effects modelled using a conditional autoregressive prior structure. The posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. A total of 708 dengue cases were notified through national surveillance between January 2016 and June 2019. Individuals aged ≤14 years were found to be 53% (95% CrI: 42%, 62%) less likely to have dengue infection than those aged >14 years. Dengue cases increased by 63% (95% CrI: 49%, 77%) for a 1°C increase in maximum temperature, and decreased by 48% (95% CrI: 25%, 64%) for a one-unit increase in normalized difference vegetation index (NDVI). There was significant residual spatial clustering after accounting for climate and environmental variables. The temporal trend was significantly higher than the national average in eastern sub-districts. The findings highlight the impact of climate and environmental variables on dengue transmission and suggests prioritizing high-risk areas for control strategies.

Temporal and area-level variation in prevalence of high-grade histologically confirmed cervical abnormalities among Indigenous and non-Indigenous women, Queensland, Australia, 2008-2017

OBJECTIVE

Despite Australia's National Cervical Screening Program, Indigenous women have a disproportionately high burden of cervical cancer. We describe temporal and area-level patterns in prevalence of histologically conformed high-grade cervical abnormalities (hHGA) among cytologically screened women by Indigenous status.

METHODS

This was a population-based study of 2,132,925 women, aged 20-69, who underwent cervical screening between 2008 and 2017, in Queensland, Australia. Of these, 47,136 were identified as Indigenous from linked hospital records. Overall patterns in hHGA prevalence by Indigenous status were quantified using prevalence rate ratios (PrRR) from negative binomial models. Bayesian spatial models were used to obtain smoothed prevalence estimates of hHGA across 528 small areas compared to the state average. Results are presented as maps and graphs showing the associated uncertainty of the estimates.

RESULTS

Overall, screened Indigenous women had significantly higher hHGA prevalence than non-Indigenous women. However, the magnitude of the difference reduced over time (p < 0.001). Adjusted for age and area-level variables, Indigenous women had 36% higher hHGA prevalence (PrRR 1.36, 95% confidence interval [1.21-1.52]) than non-Indigenous women between 2013 and 2017. The overall effect of age decreased over time (p = 0.021). Although there was evidence of moderate spatial variation in 10-year prevalence estimates for both groups of women, the high levels of uncertainty for many estimates, particularly for Indigenous women, limited our ability to draw definitive conclusions about the spatial patterns.

CONCLUSIONS

While the temporal reduction in Indigenous: non-Indigenous differential in hHGA prevalence is encouraging, further research into the key drivers of the continuing higher risk among Indigenous women is warranted.

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

Imaging the future: the emerging era of single-cell spatial proteomics

The proteome of a human cell is partitioned within organelles, such as the nucleus, and other subcellular compartments, such as the cytoplasm, forming a myriad of membrane-bound and membrane-free ultrastructures. This compartmentalization allows discrete biochemical processes to occur efficiently in isolation, with relevant proteins localized to appropriate niches to fulfill their biological function(s). Proper delivery and dynamic exchange of proteins between compartments underlie the regulation of many cellular processes, such as cell signaling, division, and programmed cell death. To this end, cells deploy dedicated trafficking mechanisms to ensure correct protein localization, as mis-localization can result in pathology. In addition to trafficking, variation in the expression, modification, and physical associations of proteins within and between cells can result in biological heterogeneity, motivating the need for single-cell measurements. In this review, we introduce diverse platform technologies developed for subcellular proteomics and high-throughput systems biology, with the aim of providing mechanistic insights into fundamental cell biological processes underlying healthy and diseased states, and valuable public data resources. In contrast to the rapidly advancing field of single-cell genomics, the single-cell spatial proteomics toolbox remains in its infancy, but is poised to make considerable advances in the coming years.

Assuming independence in spatial latent variable models: Consequences and implications of misspecification

Multivariate spatial data, where multiple responses are simultaneously recorded across spatially indexed observational units, are routinely collected in a wide variety of disciplines. For example, the Southern Ocean Continuous Plankton Recorder survey collects records of zooplankton communities in the Indian sector of the Southern Ocean, with the aim of identifying and quantifying spatial patterns in biodiversity in response to environmental change. One increasingly popular method for modeling such data is spatial generalized linear latent variable models (GLLVMs), where the correlation across sites is captured by a spatial covariance function in the latent variables. However, little is known about the impact of misspecifying the latent variable correlation structure on inference of various parameters in such models. To address this gap in the literature, we investigate how misspecifying and assuming independence for the latent variables' correlation structure impacts estimation and inference in spatial GLLVMs. Through both theory and numerical studies, we show that performance of maximum likelihood estimation and inference on regression coefficients under misspecification depends on a combination of the response type, the magnitude of true regression coefficient, and the corresponding loadings, and, most importantly, whether the corresponding covariate is (also) spatially correlated. On the other hand, estimation and inference of truly nonzero loadings and prediction of latent variables is consistently not robust to misspecification of the latent variable correlation structure.

Involvement of Striatal Direct Pathway in Visual Spatial Attention in Mice

The basal ganglia are implicated in a range of perceptual functions [1], in addition to their well-known role in the regulation of movement [2]. One unifying explanation for these diverse roles is that the basal ganglia control the level of commitment to particular motor or cognitive outcomes based on the behavioral context [3, 4]. If this explanation is applicable to the allocation of visual spatial attention, then the involvement of basal ganglia circuits should incorporate the subject's expectations about the spatial location of upcoming events as well as the routing of visual signals that guide the response. From the viewpoint of signal detection theory, these changes in the level of commitment might correspond to shifts in the subject's decision criterion, one of two distinct components recently ascribed to visual selective attention [5]. We tested this idea using unilateral optogenetic activation of neurons in the dorsal striatum of mice during a visual spatial attention task [6], taking advantage of the ability to specifically target medium spiny neurons in the "direct" pathway associated with promoting responses [7, 8]. By comparing results across attention task conditions, we found that direct-pathway activation caused changes in performance determined by the spatial probability and location of the visual event. Moreover, across conditions with identical visual stimulation, activation shifted the decision criterion selectively when attention was directed to the contralateral visual field. These results demonstrate that activity through the basal ganglia may play an important and distinct role among the multifarious mechanisms that accomplish visual spatial attention.

High-Plex and High-Throughput Digital Spatial Profiling of Non-Small-Cell Lung Cancer (NSCLC)

Profiling the tumour microenvironment (TME) has been informative in understanding the underlying tumour-immune interactions. Multiplex immunohistochemistry (mIHC) coupled with molecular barcoding technologies have revealed greater insights into the TME. In this study, we utilised the Nanostring GeoMX Digital Spatial Profiler (DSP) platform to profile a non-small-cell lung cancer (NSCLC) tissue microarray for protein markers across immune cell profiling, immuno-oncology (IO) drug targets, immune activation status, immune cell typing, and pan-tumour protein modules. Regions of interest (ROIs) were selected that described tumour, TME, and normal adjacent tissue (NAT) compartments. Our data revealed that paired analysis (n = 18) of matched patient compartments indicate that the TME was significantly enriched in CD27, CD3, CD4, CD44, CD45, CD45RO, CD68, CD163, and VISTA relative to the tumour. Unmatched analysis indicated that the NAT (n = 19) was significantly enriched in CD34, fibronectin, IDO1, LAG3, ARG1, and PTEN when compared to the TME (n = 32). Univariate Cox proportional hazards indicated that the presence of cells expressing CD3 (hazard ratio (HR): 0.5, p = 0.018), CD34 (HR: 0.53, p = 0.004), and ICOS (HR: 0.6, p = 0.047) in tumour compartments were significantly associated with improved overall survival (OS). We implemented both high-plex and high-throughput methodologies to the discovery of protein biomarkers and molecular phenotypes within biopsy samples, and demonstrate the power of such tools for a new generation of pathology research.

Habitat Shape Affects Polyploid Establishment in a Spatial, Stochastic Model

Polyploidy contributes massively to the taxonomic and genomic diversity of angiosperms, but certain aspects of polyploid evolution are still enigmatic. The establishment of a new polyploid lineage following whole-genome duplication (WGD) is a critical step for all polyploid species, but this process is difficult to identify and observe in nature. Mathematical models offer an opportunity to study this process by varying parameters related to the populations, habitats, and organisms involved in the polyploid establishment process. While several models of polyploid establishment have been published previously, very few incorporate spatial factors, including spatial relationships between organisms, habitat shape, or population density. This study presents a stochastic, spatial model of polyploid establishment that shows how factors such as habitat shape and dispersal type can influence the fixation and persistence of nascent polyploids and modulate the effects of other factors. This model predicts that narrow, constrained habitats such as roadsides and coastlines may enhance polyploid establishment, particularly in combination with frequent clonal reproduction, limited dispersal, and high population density. The similarity between this scenario and the growth of many invasive or colonizing species along disturbed, narrow habitats such as roadsides may offer a partial explanation of the prevalence of polyploidy among invasive species.

Chatting While Walking Does Not Interfere with Topographical Working Memory

In the present study, we employed the dual task technique to explore the role of language in topographical working memory when landmarks are present along the path. We performed three experiments to mainly test the effects of language but also motor, spatial motor and spatial environment interferences on topographical working memory. We aimed to clarify both the role of language in navigational working memory per se and the extent to which spatial language interferes with the main task more than the other types of interference. Specifically, in the three experiments we investigated the differences due to different verbal interference sources (i.e., articulatory suppression of nonsense syllables; right and left, up and bottom; and north, south, east and west). The main hypothesis was that the use of spatial language affected more landmark-based topographical working memory than both the verbalization of nonsense syllables and other types of interference. Results show no effect of spatial language, only spatial environmental interference affected the navigational working memory performance. In general, this might depend on the scarce role of spatial language in online navigational working memory tasks. Specifically, language is more important for learning and retrieval of the cognitive map. Implications and future research directions are discussed.

Trends in prevalence and spatiotemporal distribution of gastroschisis in Arkansas, 1998-2015

BACKGROUND

Arkansas (AR) had the highest prevalence of gastroschisis in a recent study including 15 U.S. states. Our objective was to evaluate trends in prevalence and the spatiotemporal distribution of gastroschisis in AR.

METHODS

Infants with gastroschisis, born 1998-2015, were identified from the Arkansas Reproductive Health Monitoring System. Birth record data were used as denominators for calculations. Maternal residence at delivery was geocoded for spatial analyses. Annual prevalence rates (PRs) were calculated. Joinpoint regression analysis was performed to examine trends in gastroschisis and report the annual percent changes (APCs) in PRs. Spatiotemporal analyses identified counties with unusually high PRs of gastroschisis. Poisson regression, including county, year, and county*year indicators, was fit to evaluate the PRs of gastroschisis, while adjusting for county-level maternal variables.

RESULTS

We identified 401 cases of gastroschisis among 694,459 live births. The overall PR of gastroschisis was 5.8/10,000 live births. The prevalence of gastroschisis had a significant APC of +5.3% (p < .0001) between 1998 and 2012, followed by a nonsignificant yearly average decrease of -17% through 2015 (p = 0.2). The Emerging Hot Spot Analysis and SaTScan identified an overlapping five-county cluster from 2006 to 2013. Poisson regression model, including county (inside vs. outside cluster), time (before vs. after 2006), and county*time indicators, was fit to evaluate the PRs of gastroschisis. The model did not confirm the presence of a spatiotemporal cluster, once it adjusted for county-level maternal characteristics (p = .549).

CONCLUSION

Close monitoring of rates of gastroschisis is warranted to determine if the PRs of gastroschisis continue to decline in AR.

High-Resolution Transcriptomic and Proteomic Profiling of Heterogeneity of Brain-Derived Microglia in Multiple Sclerosis

Microglia are important for central nervous system (CNS) homeostasis and first to respond to tissue damage and perturbations. Microglia are heterogeneous cells; in case of pathology, microglia adopt a range of phenotypes with altered functions. However, how these different microglia subtypes are implicated in CNS disease is largely unresolved. Multiple sclerosis (MS) is a chronic demyelinating disease of the CNS, characterized by inflammation and axonal degeneration, ultimately leading to neurological decline. One way microglia are implicated in MS is through stimulation of remyelination. They facilitate efficient remyelination by phagocytosis of myelin debris. In addition, microglia recruit oligodendrocyte precursor cells (OPCs) to demyelinated areas and stimulate remyelination. The development of high-resolution technologies to profile individual cells has greatly contributed to our understanding of microglia heterogeneity and function under normal and pathological conditions. Gene expression profiling technologies have evolved from whole tissue RNA sequencing toward single-cell or nucleus sequencing. Single microglia proteomic profiles are also increasingly generated, offering another layer of high-resolution data. Here, we will review recent studies that have employed these technologies in the context of MS and their respective advantages and disadvantages. Moreover, recent developments that allow for (single) cell profiling while retaining spatial information and tissue context will be discussed.

Influence of timeliness and receipt of first treatment on geographic variation in non-small cell lung cancer mortality

Mortality from non-small cell lung cancer (NSCLC) exhibits substantial geographical disparities. However, there is little evidence on whether this variation could be attributed to patients' clinical characteristics and/or socioeconomic inequalities. This study evaluated the independent and relative contribution of the individual- and area-level risk factors on geographic variation in 2-year all-cause mortality among NSCLC patients. In the Hierarchical-related regression approach, we used the Bayesian spatial multilevel logistic regression model to combine individual- and area-level predictors with outcomes while accounting for geographically structured and unstructured correlation. Individual-level data included 3330 NSCLC cases reported to the Victoria Lung Cancer Registry between 2011 and 2016. Area-level data comprised socioeconomic disadvantage, remoteness and pollution data at the postal area level in Victoria, Australia. With the inclusion of significant individual- and area-level risk factors, timely (≤14 days) first definitive treatment (odds ratio [OR] = 0.73, 95% credible interval [Crl] = 0.56-0.94) and multidisciplinary meetings (MDM) (OR = 0.74, 95% Crl = 0.59-0.93) showed an independent association with a lower likelihood of NSCLC 2-year all-cause mortality. Timely and delayed (>14 days) first nondefinitive treatment, no treatment, advanced clinical stage, smoking, poor performance status, public hospital insurance and area-level deprivation were independently associated with a higher likelihood of 2- and 5-year all-cause mortality. NSCLC's 2-year all-cause mortality exhibited substantial geographic variation, mainly associated with timeliness and receipt of first definitive treatment, no treatment followed by patient prognostic factors with some contribution from area-level deprivation, MDM and public hospital insurance. This study highlights NSCLC patients should receive the first definitive treatment within the recommended 14-days from diagnosis.

How Can the Lived Environment Support Healthy Ageing? A Spatial Indicators Framework for the Assessment of Age-Friendly Communities

The Age-Friendly Cities and Communities Guide was released by the World Health Organization over a decade ago with the aim of creating environments that support healthy ageing. The comprehensive framework includes the domains of outdoor spaces and buildings, transportation, housing, social participation, respect and inclusion, civic participation and employment, communication and information, and community and health services. A major critique of the age-friendly community movement has argued for a more clearly defined scope of actions, the need to measure or quantify results and increase the connections to policy and funding levers. This paper provides a quantifiable spatial indicators framework to assess local lived environments according to each Age-Friendly Cities and Communities (AFC) domain. The selection of these AFC spatial indicators can be applied within local neighbourhoods, census tracts, suburbs, municipalities, or cities with minimal resource requirements other than applied spatial analysis, which addresses past critiques of the Age-Friendly Community movement. The framework has great potential for applications within local, national, and international policy and planning contexts in the future.

Spatial Patterns of Anthrax Outbreaks and Cases among Livestock in Lesotho, 2005-2016

Background: Although anthrax occurs globally, the burden of the disease remains particularly high in Africa. Furthermore, the disease anthrax has significant public health and economic implications. However, sufficient attention has not been given to the geographic distribution of anthrax outbreaks and cases in Lesotho. Therefore, this study investigates the spatial patterns of anthrax outbreaks and cases among livestock in Lesotho from 2005 to 2016. Methods: A cross-sectional study design was adopted to realise the objectives of this study using retrospective data of anthrax outbreaks and cases recorded by the Department of Livestock Services (DLS) between 2005 and 2016. Anthrax outbreaks were geo-coded at village level and aggregated at district level. Proportions and 95% CI of anthrax outbreaks and cases by village and district were calculated. Cartographic maps displaying the distribution of anthrax outbreaks and cases at village and district level were constructed. Results: A total of 38 outbreaks were reported over the study period, and they were clustered in the Lowlands districts of Lesotho. Most outbreaks (52.6%, 20/38) in livestock were reported in the Maseru district. The Leribe district reported the lowest proportions of outbreaks (5.3%, 2/38) and cases (0.6%, 3/526). At the village level, 18% (7/38) of outbreaks were in Maseru Urban, followed by Ratau (16%, 6/38) and Mofoka (13%, 5/38). The Maseru district reported the highest (1.3%, 369/29,070) proportion of cases followed by Mafeteng (0.9%, 73/8530). The village with the most cases was Kolo (10.5%, 21/200), followed by Thaba-Chitja (7.7%, 33/430). Conclusion: Anthrax outbreaks and cases exclusively occur in the Lowlands districts of Lesotho, with villages such as Mahobong, Pitseng, Kolo, and Thaba-Chitja having a higher risk of anthrax disease. Findings of the present study have serious public health implications in light of the fact that between 2003 and 2008 Lesotho’s main abattoir was closed; hence, most of the meat in Lesotho was imported and/or sourced from the informal slaughter facilities. Much larger studies are needed to further investigate factors contributing to spatial disparities in anthrax outbreaks and cases observed in this study. Findings of the present study can be used to guide the formulation of a policy on prevention and control of anthrax in Lesotho.

Application of a Multi-Hazard Risk Assessment for Local Planning

The utility of a multi-hazard risk-scape at the county level is significant for county, state, regional, and national policy makers who rely on broad and consistent assessments of hazard exposure and losses. In previous work, the Patterns of Risk using an Integrated Spatial Multi-Hazard (PRISM) approach creates an index of county risk for this purpose. While helpful across large areas, the approach lacks information needed at more localized scales. In this paper, we employ the PRISM approach to all 2015 census tracts in the US. Use of a land-cover approach, with spatial extents and modeled data from 11 natural and 4 technological hazard types, determines spatial exposures. Furthermore, census counts allow for the estimation of population exposures in each tract by hazard type. The results of the tract-level index reveal exposure patterns that contrast the original PRISM model, with a concentration of risk shifting eastward. The distribution of land-cover and population exposure more closely resemble the county index, revealing the importance of scale and land-cover considerations, along with the need for additional investigation of risk drivers. We provide an application of the risk and multi-hazard exposures in two major metropolitan areas to demonstrate utility of the approach at this scale.

Zeb1 expression by tumor or stromal cells is associated with spatial distribution patterns of CD8+ tumor-infiltrating lymphocytes: a hypothesis-generating study on 113 triple negative breast cancers

Spatial organization of tumor microenvironment (TME) may influence tumor response to immunomodulatory therapies. Zeb1 is a driver of epithelial-mesenchymal transition, with several roles in immune cell development, however its role in shaping of the immune TME is not fully explored. We conducted a pre-multiplex spatial analysis study to verify whether Zeb1 influences spatial distribution of tumor-infiltrating lymphocytes (TILs) in triple negative breast cancer (TNBC). We applied single and double immunohistochemistry to analyze spatial relationships between CD8+, FoxP3+ and CD20+ tumor-infiltrating lymphocytes (TILs) and the cells expressing Zeb1 in formalin-fixed, paraffin-embedded surgical specimens of 113 TNBCs. 15.5% of cases had Zeb1+ tumor cells and 72.8% of cases had stroma rich in Zeb1+ cells. Low density of intratumoral CD8+ TILs was observed in almost all TNBCs with high or moderate Zeb1+ expression in tumor cells (22/23 cases, 95.6%), and in 90.4% of TNBCs (75/83 cases) with stroma rich in Zeb1+ cells. On the other side, a majority of TNBCs with stroma rich in Zeb1+ cells had high density of stromal CD8+ TILs (55/83 cases, 66.3%). These associations were not observed between Zeb1-expressing cells and FoxP3+ or CD20+ TILs. This in situ analysis showed specific spatial relationship between tumor or stromal Zeb1+ cells and CD8+ TILs, which need to be validated in other cohorts. Zeb1 was highlighted both as a marker of tumor cell EMT and of tumor stroma richness in mesenchymal cells. Several hypotheses about causes of the observed relationship between Zeb1 and TILs are generated and the approaches to verify them discussed. Zeb1 is worth further investigation as a potential biomarker of intratumor immunosuppression of TNBC and of its response to immunotherapies.

Spatial and Temporal Comparison of Perceived Risks and Confirmed Cases of Lyme Disease: An Exploratory Study of Google Trends

Non-specific symptoms in later stages of Lyme disease (LD) may mimic a variety of autoimmune, viral, or complex diseases. Patients lacking erythema migrans or who test negative under CDC guidelines, but suspect LD may search online symptoms in vein. As a result, patients with lingering and undiagnosed symptoms turn to alternative lab tests. This study addresses patient's perceived illness in relation to CDC surveillance data. Extending the literature beyond basic searches for symptoms or disease terms, this study examines spatiotemporal dynamics among symptom, disease, and unconventional lab test searches on Google Trends, in compared with CDC confirmed cases of LD. The search terms used for the Google Trends analysis between 2011 and 2015 include: (1) "lyme" and "lyme disease" for disease, (2) "tick bite," "bone pain," "stiff neck," "circular rash," and "brain fog" for symptoms, and (3) "IGENEX" for the alternative lab test. Spatial and temporal analyses illustrate noticeable similar patterns between the search frequency and the actual LD incidence. Beyond basic searches for symptoms or disease terms, we demonstrate the improved utility of Google Trends analysis in discovering spatial and temporal patterns of perceived LD and comparing with the reported LD cases. The public health and medical communities benefit from this research through improved knowledge of undiagnosed patients who are searching for alternative labs to explain lingering symptoms. This study validates the need for further research into Google Trends data and surveillance protocols of diseases characterized by non-specific symptoms, prompting patients to "self-diagnose."