Spatial distribution models of seroreactive sheep to Leptospira spp. in Veracruz, Mexico.

Leptospirosis is an infectious zoonotic disease of special importance in tropical regions of the world and is closely related to climatic conditions. In Mexico, at least eight Leptospira serogroups are known to affect sheep, but little is known about their distribution. The aim was to analyse the spatial distribution of seroreactive sheep to eight serogroups of Leptospira spp. through ecological niche modelling from the state of Veracruz. We carried out a cross-sectional, multistage, and stratified epidemiological study, sampling 405 sheep in different regions of the state (north, center, and south). The sera were analysed using the microscopic agglutination test (MAT) to identify seropositivity to eight Leptospira serogroups (Icterohaemorrhagiae, Pyrogenes, Grippotyphosa, Canicola, Pomona, Hardjo, Wolffi, and Tarassovi). Management variables in the sampled herds were evaluated through a survey among the producers, which was analysed using the chi-squared test for cross-tabulation. Geospatial modelling was conducted using MAXENT and 19 climatic variables, and validation was carried out using the area under the curve (AUC). No positive animals were found for Pomona in any area of Veracruz, and there was only one case of seroreactivity to Grippotyphosa. The total seroprevalence found was 53.83% (95% confidence interval [CI] 48.84-58.75). The main serogroup found was Sejroe (55.31%, 95% CI 50.32-60.20%), followed by Canicola (8.64%, 95% CI 6.17-11.92%), Icterohaemorrhagiae (4.69%, 95% CI 2.93-7.36%), Tarassovi (3.95%, 95% CI 2.35-6.47%), Pyrogenes (2.47%, 95% CI 1.26-4.64%), Australis (0.99%, 95% CI 0.32-2.69%), and Grippotyphosa (0.25%, 95% CI 0.01-1.59%). The predictive model for Australis was not significant. Acceptable predictive models (AUC > 0.7-0.8) were found for Canicola, Icterohaemorrhagiae, Pyrogenes, and Tarassovi, while for Sejroe, it was excellent (AUC > 0.85); consequently, the climatic variables that most contributed to the model were those related to precipitation. The potential distribution of Pyrogenes, Icterohaemorrhagiae, and Canicola was located to a greater extent in the three regions; Pyrogenes and Tarassovi were distributed mostly in the north and central regions, and Sejroe was mostly located in the center and south of the state. Ecological niche modelling could support epidemiological control and surveillance programs for affected sheep herds in the state of Veracruz.

Wireless sensor networks for pharmaceutical lyophilization: Quantification of local gas pressure and temperature in primary drying.

Wireless sensor networks have become prolific in a wide range of industrial processes and offer several key advantages over their wired counterparts in terms of positioning flexibility, modularity, interconnectivity, and data routing. We demonstrate their utility in pharmaceutical lyophilization by developing a series of wireless devices to measure spatial variations in gas pressure and temperature during primary drying. The influence of shelf temperature, chamber pressure, excipient concentration, and dryer configuration are explored for various representative cycles using a laboratory-scale pharmaceutical lyophilizer. Pressure and temperature variations across the shelf for these cases are shown to vary up to 1.2 Pa and 10 °C, respectively. Experimental measurements are supported by computational fluid dynamics simulations to reveal the mechanisms driving the vapor flow. The measurements and simulation data are then combined to estimate the shelf-wise sublimation rate in the inverse sense to within a deviation of 3% based on comparison with gravimetric data. We then apply the sublimation rate profile to obtain the vial heat transfer coefficient and product mass transfer resistance for a 5% w/v mannitol formulation. Finally, these parameters are applied to a one-dimensional quasi-steady heat transfer model to predict the evolution of the product temperature over the course of primary drying. Thermocouple measurements of product temperature are compared directly to the simulated data and demonstrate accuracy comparable to existing published one-dimensional models.

SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies.

Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.

Strategies on locations of public access defibrillator: A systematic review.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a critical condition with poor outcomes. Although the survival rate increases in those who undergo defibrillation, the utility of on-time defibrillation among bystanders remained low. An evaluation of the deployment strategy for public access defibrillators (PADs) is necessary to increase their use and accessibility. This study was to conduct a systematic review for deployment strategies of PADs. METHODS: Two authors independently searched for articles published before October 2019 from PubMed, Embase, Web of Science, and Cochrane Library. An independent librarian provided the search strategy and assisted the literature research. We included articles that were focused on the main topic, but excluded those which were missing results or that used an unclear definition. The qualitative outcomes were the utility and OHCA coverage of PADs. We performed a qualitative analysis across the studies, but a quantitative analysis was not available due to the studies' heterogeneity in design and variety of outcomes. RESULTS: We eventually included 15 studies. Three strategies were presented: guidelines-based, grid-based, and landmark-based. The guidelines-based deployment was common fit for OHCA events. The grid-based method increased the use of bystander defibrillation 3-fold, and 30-day survival doubled. The top 3 landmarks in the landmark-based strategy were offices (18.6%), schools (13.3%), and sports facilities (12.9%). Utility of PADs might increase if we optimize PAD location by mathematical modeling and evaluation feedback. CONCLUSION: Three deployment strategies were presented. Although the optimal method could not be fully identified, a more efficient PAD deployment could benefit the population in terms of OHCA coverage and survival among patients with OHCA.

StationRank: Aggregate dynamics of the Swiss railway.

Increasing availability and quality of actual, as opposed to scheduled, open transport data offers new possibilities for capturing the spatiotemporal dynamics of railway and other networks of social infrastructure. One way to describe such complex phenomena is in terms of stochastic processes. At its core, a stochastic model is domain-agnostic and algorithms discussed here have been successfully used in other applications, including Google's PageRank citation ranking. Our key assumption is that train routes constitute meaningful sequences analogous to sentences of literary text. A corpus of routes is thus susceptible to the same analytic tool-set as a corpus of sentences. With our experiment in Switzerland, we introduce a method for building Markov Chains from aggregated daily streams of railway traffic data. The stationary distributions under normal and perturbed conditions are used to define systemic risk measures with non-evident, valuable information about railway infrastructure.

Copula geoadditive modelling of anaemia and malaria in young children in Kenya, Malawi, Tanzania and Uganda.

BACKGROUND: Anaemia and malaria are the leading causes of sub-Saharan African childhood morbidity and mortality. This study aimed to explore the complex relationship between anaemia and malaria in young children across the districts or counties of four contiguous sub-Saharan African countries, namely Kenya, Malawi, Tanzania and Uganda, while accounting for the effects of socio-economic, demographic and environmental factors. Geospatial maps were constructed to visualise the relationship between the two responses across the districts of the countries. METHODS: A joint bivariate copula regression model was used, which estimates the correlation between the two responses conditional on the linear, non-linear and spatial effects of the explanatory variables considered. The copula framework allows the dependency structure between the responses to be isolated from their marginal distributions. The association between the two responses was set to vary according to the district of residence across the four countries. RESULTS: The study revealed a positive association between anaemia and malaria throughout the districts, the strength of which varied across the districts of the four countries. Due to this heterogeneous association between anaemia and malaria, we further considered the joint probability of each combination of outcome of anaemia and malaria to further reveal more about the relationship between the responses. A considerable number of districts had a high joint probability of a child being anaemic but not having malaria. This might suggest the existence of other significant drivers of childhood anaemia in these districts. CONCLUSIONS: This study presents an alternative technique to joint modelling of anaemia and malaria in young children which assists in understanding more about their relationship compared to techniques of multivariate modelling. The approach used in this study can aid in visualising the relationship through mapping of their correlation and joint probabilities. These maps produced can then help policy makers target the correct set of interventions, or prevent the use of incorrect interventions, particularly for childhood anaemia, the causes of which are multiple and complex.

SnapShot: The Regulatory Genome.

Enhancers switch genes on and off in response to a variety of intrinsic and external cellular signals. They are the cornerstone of gene regulation and the most pervasive constituents of the regulatory genome. Sequence polymorphisms in enhancer DNAs are a major source of population diversity and predilection to disease. To view this SnapShot, open or download the PDF.

Citizen science for predicting spatio-temporal patterns in seabird abundance during migration.

Pelagic seabirds are elusive species which are difficult to observe, thus determining their spatial distribution during the migration period is a difficult task. Here we undertook the first long-term study on the distribution of migrating shearwaters from data gathered within the framework of citizen science projects. Specifically, we collected daily abundance (only abundance given presence) of Balearic shearwaters from 2005 to 2017 from the online databases Trektellen and eBird. We applied machine-learning techniques, specifically Random Forest regression models, to predict shearwater abundance during migration using 15 environmental predictors. We built separated models for pre-breeding and post-breeding migration. When evaluated for the total data sample, the models explained more than 52% of the variation in shearwater abundance. The models also showed good ability to predict shearwater distributions for both migration periods (correlation between observed and predicted abundance was about 70%). However, relative variable importance and variation among the models built with different training data subsamples differed between migration periods. Our results showed that data gathered in citizen science initiatives together with recently available high-resolution satellite imagery, can be successfully applied to describe the migratory spatio-temporal patterns of seabird species accurately. We show that a predictive modelling approach may offer a powerful and cost-effective tool for the long-term monitoring of the migratory patterns in sensitive marine species, as well as to identify at sea areas relevant for their protection. Modelling approaches can also be essential tools to detect the impacts of climate and other global changes in this and other species within the range of the training data.

Social interactions drive efficient foraging and income equality in groups of fish.

The social interactions underlying group foraging and their benefits have been mostly studied using mechanistic models replicating qualitative features of group behavior, and focused on a single resource or a few clustered ones. Here, we tracked groups of freely foraging adult zebrafish with spatially dispersed food items and found that fish perform stereotypical maneuvers when consuming food, which attract neighboring fish. We then present a mathematical model, based on inferred functional interactions between fish, which accurately describes individual and group foraging of real fish. We show that these interactions allow fish to combine individual and social information to achieve near-optimal foraging efficiency and promote income equality within groups. We further show that the interactions that would maximize efficiency in these social foraging models depend on group size, but not on food distribution, and hypothesize that fish may adaptively pick the subgroup of neighbors they 'listen to' to determine their own behavior.

Could climate trends disrupt the contact rates between Ixodes ricinus (Acari, Ixodidae) and the reservoirs of Borrelia burgdorferi s.l.?

This study addresses the modifications that future climate conditions could impose on the transmission cycles of Borrelia burgdorferi s.l. by the tick Ixodes ricinus in Europe. Tracking the distribution of foci of a zoonotic agent transmitted by vectors as climate change shapes its spatial niche is necessary to issue self-protection measures for the human population. We modeled the current distribution of the tick and its predicted contact rates with 18 species of vertebrates known to act as reservoirs of the pathogen. We approached an innovative way for estimating the possibility of permanent foci of Borrelia afzelii or Borrelia garinii tracking separately the expected spatial overlap among ticks and reservoirs for these pathogens in Europe. Environmental traits were obtained from MODIS satellite images for the years 2002-2017 (baseline) and projected on scenarios for the years 2030 and 2050. The ratio between MODIS baseline/current interpolated climatologies (WorldClim), and the ratio between MODIS-projected year 2050 with five climate change scenarios for that year (WorldClim) revealed no significant differences, meaning that projections from MODIS are reliable. Models predict that contact rates between the tick and reservoirs of either B. garinii or B. afzelii are spatially different because those have different habitats overlap. This is expected to promote different distribution patterns because of the different responses of both groups of reservoirs to environmental variables. Models for 2030 predict an increase in latitude, mainly in the circulation of B. garinii, with large areas of expected permanent contact between vector and reservoirs in Nordic countries and central Europe. However, climate projections for the year 2050 predict an unexpected scenario of contact disruption. Though large areas in Europe would be suitable for circulation of the pathogens, the predicted lack of niche overlap among ticks and reservoirs could promote a decrease in permanent foci. This development represents a proof-of-concept for the power of jointly modeling both the vector and reservoirs in a common framework. A deeper understanding of the unanticipated result regarding the year 2050 is needed.

Impacts of Zika emergence in Latin America on endemic dengue transmission.

In 2015 and 2016, Zika virus (ZIKV) swept through dengue virus (DENV) endemic areas of Latin America. These viruses are of the same family, share a vector and may interact competitively or synergistically through human immune responses. We examine dengue incidence from Brazil and Colombia before, during, and after the Zika epidemic. We find evidence that dengue incidence was atypically low in 2017 in both countries. We investigate whether subnational Zika incidence is associated with changes in dengue incidence and find mixed results. Using simulations with multiple assumptions of interactions between DENV and ZIKV, we find cross-protection suppresses incidence of dengue following Zika outbreaks and low periods of dengue incidence are followed by resurgence. Our simulations suggest correlations in DENV and ZIKV reproduction numbers could complicate associations between ZIKV incidence and post-ZIKV DENV incidence and that periods of low dengue incidence are followed by large increases in dengue incidence.

Spectral tuning of adaptation supports coding of sensory context in auditory cortex.

Perception of vocalizations and other behaviorally relevant sounds requires integrating acoustic information over hundreds of milliseconds. Sound-evoked activity in auditory cortex typically has much shorter latency, but the acoustic context, i.e., sound history, can modulate sound evoked activity over longer periods. Contextual effects are attributed to modulatory phenomena, such as stimulus-specific adaption and contrast gain control. However, an encoding model that links context to natural sound processing has yet to be established. We tested whether a model in which spectrally tuned inputs undergo adaptation mimicking short-term synaptic plasticity (STP) can account for contextual effects during natural sound processing. Single-unit activity was recorded from primary auditory cortex of awake ferrets during presentation of noise with natural temporal dynamics and fully natural sounds. Encoding properties were characterized by a standard linear-nonlinear spectro-temporal receptive field (LN) model and variants that incorporated STP-like adaptation. In the adapting models, STP was applied either globally across all input spectral channels or locally to subsets of channels. For most neurons, models incorporating local STP predicted neural activity as well or better than LN and global STP models. The strength of nonlinear adaptation varied across neurons. Within neurons, adaptation was generally stronger for spectral channels with excitatory than inhibitory gain. Neurons showing improved STP model performance also tended to undergo stimulus-specific adaptation, suggesting a common mechanism for these phenomena. When STP models were compared between passive and active behavior conditions, response gain often changed, but average STP parameters were stable. Thus, spectrally and temporally heterogeneous adaptation, subserved by a mechanism with STP-like dynamics, may support representation of the complex spectro-temporal patterns that comprise natural sounds across wide-ranging sensory contexts.

Behavioral and neural aspects of the spatial processing of conspecifics signals in the electrosensory system.

Localizing the source of a signal is often as important as deciphering the signal's message. Localization mechanisms must cope with the challenges of representing the spatial information of weak, noisy signals. Comparing these strategies across modalities and model systems allows a broader understanding of the general principles shaping spatial processing. In this review we focus on the electrosensory system of knifefish and provide an overview of our current understanding of spatial processing in this system, in particular, localization of conspecific signals. We argue that many mechanisms observed in other sensory systems, such as the visual or auditory systems, have comparable implementations in the electrosensory system. Our review therefore describes a field of research with unique opportunities to provide new insights into the principles underlying spatial processing. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The ecological drivers of variation in global language diversity.

Language diversity is distributed unevenly over the globe. Intriguingly, patterns of language diversity resemble biodiversity patterns, leading to suggestions that similar mechanisms may underlie both linguistic and biological diversification. Here we present the first global analysis of language diversity that compares the relative importance of two key ecological mechanisms - isolation and ecological risk - after correcting for spatial autocorrelation and phylogenetic non-independence. We find significant effects of climate on language diversity, consistent with the ecological risk hypothesis that areas of high year-round productivity lead to more languages by supporting human cultural groups with smaller distributions. Climate has a much stronger effect on language diversity than landscape features, such as altitudinal range and river density, which might contribute to isolation of cultural groups. The association between biodiversity and language diversity appears to be an incidental effect of their covariation with climate, rather than a causal link between the two.

Interactions between a population of fallow deer (Dama dama), humans and crops in a managed composite temperate landscape in southern Sweden: Conflict or opportunity?

Landscapes composed of agricultural land mixed with forest are desirable since they provide a wide range of diversified ecosystem services, unlike specialized agricultural landscapes, but that creates a trade-off between these land uses since wildlife usually feed on crops and reduce yields. In Nordic countries, where human population density is low and game hunting can be a viable economic alternative, mixed landscape systems are particularly interesting. To evaluate the economic sustainability of such systems we need to quantify wildlife damage to crops. One important species, being popular among Swedish hunters and therefore economically valuable, is fallow deer (Dama dama). Our objective was to evaluate the economic sustainability of mixed landscape systems including cultivated fields and commercial hunting of fallow deer. We studied the effects of excluding fallow deer by using 86 exclosures and adjacent plots in winter wheat and oat fields in south-west Sweden. We analyzed yield losses and interactions between spatial and temporal grazing patterns, anthropogenic landscape features, and topological characteristics of the landscape. We found that animals avoided exposed spots, irrespective of distance from human activity. We also found a seasonal grazing pattern related to the different growing periods of winter wheat (more grazed, emerging in autumn) and spring oat (less grazed, emerging in spring). We then compared the costs of crop damage against the commercial value of fallow deer hunting. The damage amounted to 375 ±196 € ha-1 for wheat and 152 ±138 € ha-1 for oat, corresponding to a total cost per animal of 82.7 ±81.0 €, while each animal had an estimated market value of approximately 100 €. Therefore the value of fallow deer presence compensated for the associated cost of crop damage. Profit could be further improved in this case by adopting additional management strategies. In general our study confirmed the economic feasibility of this particular mixed land management.

Exploring Spatial Influence of Remotely Sensed PM2.5 Concentration Using a Developed Deep Convolutional Neural Network Model.

Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for exploiting the spatial influence feature in remotely sensed data. The method was applied in investigating the magnitude of the spatial influence of four factors-population, gross domestic product (GDP), terrain, land-use and land-cover (LULC)-on remotely sensed PM2.5 concentration over China. Satisfactory results were produced by the method. It demonstrates that the deep CNN model can be well applied in the field of spatial analysing remotely sensed big data. And the accuracy of the deep CNN is much higher than of geographically weighted regression (GWR) based on comparation. The results showed that population spatial density, GDP spatial density, terrain, and LULC could together determine the spatial distribution of PM2.5 annual concentrations with an overall spatial influencing magnitude of 97.85%. Population, GDP, terrain, and LULC have individual spatial influencing magnitudes of 47.12% and 36.13%, 50.07% and 40.91% on PM2.5 annual concentrations respectively. Terrain and LULC are the dominating spatial influencing factors, and only these two factors together may approximately determine the spatial pattern of PM2.5 annual concentration over China with a high spatial influencing magnitude of 96.65%.

Heavy metal contamination assessment of surface sediments of the Subei Shoal, China: Spatial distribution, source apportionment and ecological risk.

This study investigated heavy metals (Ni, Zn, Cr, Cu, As, Pb, Cd and Hg) concentrations in surface sediment of Subei Shoal, China, to illustrate their spatial distribution characteristics, sources and potential ecological risk of pollution. Contents of total organic carbon (TOC), clay, silt and sand were 1.7 ±â€¯0.8%, 3.3 ±â€¯3.2%, 13.6 ±â€¯14.2% and 83.1 ±â€¯17.4%, respectively. The spatial distribution of TOC, clay and silt were similar; however, distribution of Hg was inverse. Concentrations of Ni, Zn, Cr, Cu, As, Pb, Cd and Hg were 47.88 ±â€¯8.93, 38.18 ±â€¯8.86, 19.22 ±â€¯5.14, 11.32 ±â€¯5.07, 6.97 ±â€¯2.45, 0.13 ±â€¯0.72, 0.56 ±â€¯0.77 and 0.06 ±â€¯0.02 mg kg-1 sediment, respectively. Principal component analysis suggested that Cu, Zn, Cd, Cr and Ni were mainly derived from natural sources, whereas Pb, Cd, As and Hg from industrial and agricultural sources. Results of geo-accumulation index, potential ecological risk index (RI), pollution load index (PLI), toxic risk index (TRI) and contamination severity index (CSI) demonstrated that pollution levels of Cd and Hg were moderate, which should attract more attention as main pollution factors. The pollution was mainly distributed in the central and northern parts, and the southern part had a good ecological environment. Moreover, the contaminated stations accounted respective for 33.4%, 25.9%, 33.3% and 70.4% of RI, PLI, TRI and CSI, of which 70.4% of the contamination severity index stations contained 66.7% of much lower severity stations. These findings could contribute to more effective exploitation of tidal flat resources, and the prevention and treatment of tidal marsh environment.

Evidence for the widespread occurrence of short- and medium-chain chlorinated paraffins in fish collected from the Rhône River basin (France).

Chlorinated paraffins (CPs) are high-volume chemicals used in numerous industrial applications. Their quantitative analysis is extremely challenging and this work presents the optimization of an analytical method based on gas chromatography hyphenated with electron capture negative ionization time-of-flight high-resolution mass spectrometry (GC-ECNI-TOF HRMS) for the simultaneous determination of short-chain and medium-chain CPs (SCCPs and MCCPs, respectively) in fish tissues (i.e. dorsal muscle). The resolution of the TOF-MS analyzer reduced or eliminated isobaric interferences and the CP response was optimized through Design of Experiment. A simple clean-up procedure based on adsorption chromatography further removed some potentially interfering organochlorines. Good selectivity, linearity and accuracy were achieved; method detection limits or limits of reporting were compatible with expected levels in wild fish (0.03-0.35 ng g-1 wet weight, ww, depending on the congener). This method was proven suitable for the analysis of CPs in tissues of common barbel Barbus, a fish species frequently used for water quality monitoring purposes in Europe. SCCPs and MCCPs were found to be widespread within the Rhône river basin (France). At all locations, MCCP concentrations (1.3-72.7 ng g-1 ww) were higher than those of SCCPs (0.3-10.6 ng g-1 ww) and levels were systematically lower than the proposed Predicted No Effect Concentrations (PNECs). Spatial variations of SCCP composition profiles largely surpassed those of MCCPs, suggesting the influence of local sources.

Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model.

Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM10 (PM < 10 µm), fine (PM < 2.5 µm, PM2.5) and coarse particles (PM between 2.5 and 10 µm, PM2.5-10) at 1-km2 grid for 2013-2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM2.5 and PM2.5-10 concentrations in monitors where only PM10 data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km2 grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM10 and PM2.5, respectively. Model fitting was less optimal for PM2.5-10, in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects.

Moderating Effects of Community Social Capital on Depression in Later Years of Life: A Latent Interaction Model.

OBJECTIVE: This study tested the stress-buffering model and examined the buffering role of community social capital on late-life depression. METHODS: This study used the data from the second wave of National Social Life, Health, and Aging Project (NSHAP, 2010-2012). In the present study, a total of 2,362 older adults aged 65 and older (Mage = 74.5, SD = 6.69) were included. Latent moderated structural equations model was tested by comparing the main effect model and interaction model. Depression, stress, and community social capital were constructed as latent variables for the analyses. RESULTS: The main effect model was acceptable: χ2 (df = 334) = 1596.4, p = .000; RMSEA = .04 (.038 - .042); CFI = .91; and SRMR = .05. And interaction model was significant (D = 35.0, p < .001). The latent constructs of stress (ß = . 50, p < .001) and community social capital (ß = -.14, p < .001) not only had a direct effect on depression, but their interaction was also significant (ß = -.21, p < .01).). The group with a high level of social capital presented a relatively stable slope in the prediction of stress on depression, suggesting their resilience, while the group with a low level of community social capital demonstrated a steep slope, indicating heighten vulnerability to depression when faced with stress. CONCLUSIONS: The findings support the hypothesis of stress buffering model and identify the protective effects of community social capital on depression of older adults. CLINICAL IMPLICATIONS: Older adults with lower community social capital are particularly vulnerable to depression. The results highlight that practitioners and policymakers should pay more attention to finding ways to enhance community resources to improve older adults' mental health.