Assessing the 2023 Canadian wildfire smoke impact in Northeastern US: Air quality, exposure and environmental justice.

The Canadian wildfires in June 2023 significantly impacted the northeastern United States, particularly in terms of worsened air pollution and environmental justice concerns. While advancements have been made in low-cost sensor deployments and satellite observations of atmospheric composition, integrating dynamic human mobility with wildfire PM2.5 exposure to fully understand the environmental justice implications remains underinvestigated. This study aims to enhance the accuracy of estimating ground-level fine particulate matter (PM2.5) concentrations by fusing chemical transport model outputs with empirical observations, estimating exposures using human mobility data, and evaluating the impact of environmental justice. Employing a novel data fusion technique, the study combines the Weather Research and Forecasting model with Chemistry (WRF-Chem) outputs and surface PM2.5 measurements, providing a more accurate estimation of PM2.5 distribution. The study addresses the gap in traditional exposure assessments by incorporating human mobility data and further investigates the spatial correlation of PM2.5 levels with various environmental and demographic factors from the US Environmental Protection Agency (EPA) Environmental Justice Screening and Mapping Tool (EJScreen). Results reveal that despite reduced mobility during high PM2.5 levels from wildfire smoke, exposure for both residents and individuals on the move remains high. Regions already burdened with high environmental pollution levels face amplified PM2.5 effects from wildfire smoke. Furthermore, we observed mixed correlations between PM2.5 concentrations and various demographic and socioeconomic factors, indicating complex exposure patterns across communities. Urban areas, in particular, experience persistent high exposure, while significant correlations in rural areas with EJScreen factors highlight the unique vulnerabilities of these populations to smoke exposure. These results advocate for a comprehensive approach to environmental health that leverages advanced models, integrates human mobility data, and addresses socio-demographic disparities, contributing to the development of equitable strategies against the growing threat of wildfires.

Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States.

Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources.

Residential Segregation and County-Level COVID-19 Booster Coverage in the Deep South: Surveillance Report and Ecological Study.

BACKGROUND: COVID-19 had a greater impact in the Deep South compared with other regions in the United States. While vaccination remains a top priority for all eligible individuals, data regarding the progress of booster coverage in the Deep South and how the coverage varies by county and age are sparse. Despite existing evidence of racial and ethnic disparities in COVID-19 vaccinations at the individual level, there is an urgent need for evidence at the population level. Such information could highlight vulnerable communities and guide future health care policy-making and resource allocation. OBJECTIVE: We aimed to evaluate county-level COVID-19 booster coverage by age group in the Deep South and explore its association with residential segregation. METHODS: An ecological study was conducted at the population level by integrating COVID-19 vaccine surveillance data, residential segregation index, and county-level factors across the 418 counties of 5 Deep South states from December 15, 2021, to October 19, 2022. We analyzed the cumulative percentages of county-level COVID-19 booster uptake by age group (eg, 12 to 17 years, 18 to 64 years, and at least 65 years) by the end of the study period. The longitudinal relationships were examined between residential segregation, the interaction of time and residential segregation, and COVID-19 booster coverage using the Poisson model. RESULTS: As of October 19, 2022, among the 418 counties, the median of booster uptake was 40% (IQR 37.8%-43%). Compared with older adults (ie, at least 65 years; median 63.1%, IQR 59.5%-66.5%), youth (ie, 12 to 17 years; median 14.1%, IQR 11.3%-17.4%) and adults (ie, 18 to 64 years; median 33.4%, IQR 30.5%-36.5%) had lower percentages of booster uptake. There was geospatial heterogeneity in the county-level COVID-19 booster coverage. We found that higher segregated counties had lower percentages of booster coverage. Such relationships attenuated as time increased. The findings were consistent across the age groups. CONCLUSIONS: The progress of county-level COVID-19 booster coverage in the Deep South was slow and varied by age group. Residential segregation precluded the county-level COVID-19 booster coverage across age groups. Future efforts regarding vaccination strategies should focus on youth and adults. Health care facilities and resources are needed in racial and ethnic minority communities.

Phase 1 Open-Label Dose Escalation Trial for the Development of a Human Bacillus Calmette-Guérin Challenge Model for Assessment of Tuberculosis Immunity In Vivo.

BACKGROUND: A controlled human infection model for assessing tuberculosis (TB) immunity can accelerate new vaccine development. METHODS: In this phase 1 dose escalation trial, 92 healthy adults received a single intradermal injection of 2 × 106 to 16 × 106 colony-forming units of Bacillus Calmette-Guérin (BCG). The primary endpoints were safety and BCG shedding as measured by quantitative polymerase chain reaction, colony-forming unit plating, and MGIT BACTEC culture. RESULTS: Doses up to 8 × 106 were safe, and there was evidence for increased BCG shedding with dose escalation. The MGIT time-to-positivity assay was the most consistent and precise measure of shedding. Power analyses indicated that 10% differences in MGIT time to positivity (area under the curve) could be detected in small cohorts (n = 30). Potential biomarkers of mycobacterial immunity were identified that correlated with shedding. Transcriptomic analysis uncovered dose- and time-dependent effects of BCG challenge and identified a putative transcriptional TB protective signature. Furthermore, we identified immunologic and transcriptomal differences that could represent an immune component underlying the observed higher rate of TB disease incidence in males. CONCLUSIONS: The safety, reactogenicity, and immunogenicity profiles indicate that this BCG human challenge model is feasible for assessing in vivo TB immunity and could facilitate the vaccine development process. CLINICAL TRIALS REGISTRATION: NCT01868464 (ClinicalTrials.gov).

Place Visitation Data Reveals the Geographic and Racial Disparities of COVID-19 Impact on HIV Service Utilization in the Deep South.

BACKGROUND: The COVID-19 pandemic has posed unprecedented pressure to health care systems, and interrupted health care delivery and access including HIV care in the United States' Deep South, which endures a double epidemic of HIV and COVID-19. Ryan White programs cover HIV care services for over half of PLWH in the Deep South. Given the important role of Ryan White programs, examining the visitation changes to Ryan White facilities during the pandemic offers insights into the impact of the pandemic on HIV healthcare utilization. OBJECTIVES: Analyze the geographic distribution of HIV facility visitors at the county level before and during the pandemic in the nine US states of Deep South (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas) to reveal the geographic and racial disparity in visitation disruption caused by the pandemic. METHODS: We first extracted mobile device-based visitation data for Ryan White HIV facilities in the Deep South during 2019 and 2020. To quantify the disruption in visitations during 2020, we calculated the visitation reduction rate (VRR) for each county, using 2019 data as the baseline. Next, we conducted a spatial analysis of the VRR values to uncover geographical disparities in visitation interruptions. To investigate racial disparities, we performed spatial regression analyses with VRR as the dependent variable, and the percentages of Black, Hispanic, and Asian populations as the independent variables. In this analysis, we controlled for potential confounders. RESULTS: Geographic disparities in visitation reduction were observed, with all nine Deep South states experiencing significant drops. Georgia experienced the highest visitation loss (VRR = -0.58), followed by Texas (-0.47), Alabama (0.47), and Tennessee (-0.46), while South Carolina had the smallest reductions (-0.11). All the regression models consistently revealed racial disparities in visitation interruption. That is, counties with a higher proportion of Black population tended to have higher RW facility visitation reductions. CONCLUSIONS: Our analysis revealed distinct geographic disparities in visitation interruptions at Ryan White HIV facilities in the Deep South during the COVID-19 pandemic in 2020. Furthermore, we found that the Black/African American population experienced a greater disruption at the county level in the Deep South during this period.

Association between immigrant concentration and mental health service utilization in the United States over time: A geospatial big data analysis.

Immigrants (foreign-born United States [US] citizens) generally have lower utilization of mental health services compared with US-born counterparts, but extant studies have not investigated the disparities in mental health service utilization within immigrant population nationwide over time. Leveraging mobile phone-based visitation data, we estimated the average mental health utilization in contiguous US census tracts in 2019, 2020, and 2021 by employing two novel outcomes: mental health service visits and visit-to-need ratio (i.e., visits per depression diagnosis). We then investigated the tract-level association between immigration concentration and mental health service utilization outcomes using mixed-effects linear regression models that accounted for spatial lag effects, time effects, and covariates. This study reveals spatial and temporal disparities in mental health service visits and visit-to-need ratio among different levels of immigrant concentration across the US, both before and during the pandemic. Tracts with higher concentrations of Latin American immigrants showed significantly lower mental health service utilization visits and visit-to-need ratio, particularly in the US West. Tracts with Asian and European immigrant concentrations experienced a more significant decline in mental health service utilization visits and visit-to-need ratio from 2019 to 2020 than those with Latin American concentrations. Meanwhile, in 2021, tracts with Latin American concentrations had the least recovery in mental health service utilization visits. The study highlights the potential of geospatial big data for mental health research and informs public health interventions.

Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022.

BACKGROUND: Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people's expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. OBJECTIVE: This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. METHODS: This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. RESULTS: Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. CONCLUSIONS: Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner.

Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A case study of South Carolina.

Direct human physical contact accelerates COVID-19 transmission. Smartphone mobility data has emerged as a valuable data source for revealing fine-grained human mobility, which can be used to estimate the intensity of physical contact surrounding different locations. Our study applied smartphone mobility data to simulate the second wave spreading of COVID-19 in January 2021 in three major metropolitan statistical areas (Columbia, Greenville, and Charleston) in South Carolina, United States. Based on the simulation, the number of historical county-level COVID-19 cases was allocated to neighborhoods (Census block groups) and points of interest (POIs), and the transmission rate of each allocated place was estimated. The result reveals that the COVID-19 infections during the study period mainly occurred in neighborhoods (86%), and the number is approximately proportional to the neighborhood's population. Restaurants and elementary and secondary schools contributed more COVID-19 infections than other POI categories. The simulation results for the coastal tourism Charleston area show high transmission rates in POIs related to travel and leisure activities. The results suggest that neighborhood-level infectious controlling measures are critical in reducing COVID-19 infections. We also found that households of lower socioeconomic status may be an umbrella against infection due to fewer visits to places such as malls and restaurants associated with their low financial status. Control measures should be tailored to different geographic locations since transmission rates and infection counts of POI categories vary among metropolitan areas.

Effects of Bacillus subtilis BS-Z15 on Intestinal Microbiota Structure and Body Weight Gain in Mice.

In our previous study, we identified a metabolite of Bacillus subtilis BS-Z15 (a strain with probiotic characteristics) that could improve immunity in mice. In the present study, we examined the effects of B. subtilis BS-Z15 and its metabolites on body weight gain and the intestinal microbiota of mice. Sixty 25-day-old male Kunming white mice were selected and randomly divided into four groups: control group (A), daily saline gavage; B. subtilis-treated group (B), single gavage (1 × 109 CFU/time/animal/day); group D, 14 consecutive gavages (1 × 109 CFU/time/animal/day); and B. subtilis metabolite-treated group (E), 30 consecutive gavages (90 mg kg-1/time/animal/day). High-throughput sequencing technology was used to analyze intergroup differences in the mouse intestinal microbiota. The results showed that the three treated groups had significantly slower body weight gain compared with the control group, which lasted until the 45 days (P < 0.05), and the daily food intake of the treated mice was higher (P < 0.05). The intestinal microbiota structure of the mice in the treated groups was significantly altered compared with that in the control group, suggesting that B. subtilis BS-Z15 may regulate the weight gain of animals by affecting their intestinal bacterial composition. After stopping the gavage of B. subtilis BS-Z15, the abundance of this strain in the small intestine of the mice gradually decreased and its presence was undetectable at 45 days, indicating that B. subtilis BS-Z15 could not colonize the intestine of these mice. These findings suggest that B. subtilis BS-Z15 may regulate intestinal microbiota through its metabolites to reduce weight gain.

Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A Case Study of South Carolina.

Direct human physical contact accelerates COVID-19 transmission. Smartphone mobility data has been an emerging data source to reveal fine-grained human mobility, which can be used to estimate the intensity of physical contact surrounding different locations. Our study applied smartphone mobility data to simulate the second wave spreading of COVID-19 in January 2021 in three major metropolitan statistical areas (Columbia, Greenville, and Charleston) in South Carolina, United States. Based on the simulation, the number of historical county-level COVID-19 cases was allocated to neighborhoods (Census blockgroups) and points of interest (POIs), and the transmission rate of each allocated place was estimated. The result reveals that the COVID-19 infections during the study period mainly occurred in neighborhoods (86%), and the number is approximately proportional to the neighborhood’s population. Restaurants and elementary and secondary schools contributed more COVID-19 infections than other POI categories. The simulation results for the coastal tourism Charleston area show high transmission rates in POIs related to travel and leisure activities. The results suggest that the neighborhood-level infectious controlling measures are critical in reducing COVID-19 infections. We also found that the households of lower socioeconomic status may be an umbrella against infection due to fewer visits to places such as malls and restaurants associated with their low financial status. Control measures should be tailored to different geographic locations since transmission rates and infection counts of POI categories vary among metropolitan areas.

The times, they are a-changin': tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia.

INTRODUCTION: Widespread problems of psychological distress have been observed in many countries following the outbreak of COVID-19, including Australia. What is lacking from current scholarship is a national-scale assessment that tracks the shifts in mental health during the pandemic timeline and across geographic contexts. METHODS: Drawing on 244 406 geotagged tweets in Australia from 1 January 2020 to 31 May 2021, we employed machine learning and spatial mapping techniques to classify, measure and map changes in the Australian public's mental health signals, and track their change across the different phases of the pandemic in eight Australian capital cities. RESULTS: Australians' mental health signals, quantified by sentiment scores, have a shift from pessimistic (early pandemic) to optimistic (middle pandemic), reflected by a 174.1% (95% CI 154.8 to 194.5) increase in sentiment scores. However, the signals progressively recessed towards a more pessimistic outlook (later pandemic) with a decrease in sentiment scores by 48.8% (95% CI 34.7 to 64.9). Such changes in mental health signals vary across capital cities. CONCLUSION: We set out a novel empirical framework using social media to systematically classify, measure, map and track the mental health of a nation. Our approach is designed in a manner that can readily be augmented into an ongoing monitoring capacity and extended to other nations. Tracking locales where people are displaying elevated levels of pessimistic mental health signals provide important information for the smart deployment of finite mental health services. This is especially critical in a time of crisis during which resources are stretched beyond normal bounds.

Moderation effect of community health on the relationship between racial/ethnic residential segregation and HIV viral suppression in South Carolina: A county-level longitudinal study from 2013 to 2018.

Background: Viral suppression is the ultimate goal of the HIV treatment cascade and a primary endpoint of antiretroviral therapy. Empirical evidence found racial/ethnic disparities in viral suppression among people living with HIV (PWH), but the evidence of the relationship between racial/ethnic residential segregation and place-based viral suppression is scarce. Further exploring potential structural moderators in this relationship has substantial implications for healthcare policymaking and resource allocation. The current study aimed to investigate the spatial-temporal disparities in the HIV viral suppression rate across 46 counties in South Carolina from 2013 to 2018. We also examined the impact of racial/ethnic residential segregation and the moderation effect of community health, one measurement of community engagement and volunteerism. Methods: The proportion of PWH who achieved viral suppression for each county and calendar year was calculated using de-identified electronic medical records. The isolation index was calculated and used to measure racial/ethnic residential segregation. The community health index and other county-level factors were directly extracted from multiple publicly available datasets. We used geospatial mapping to explore the spatial-temporal variations of HIV viral suppression rates. Hierarchical quasi-binominal regression models were used to examine the impacts of racial/ethnic residential segregation on county-level viral suppression rate by the extent of community health. Results: From 2013 to 2018, the average viral suppression rate across 46 counties in SC increased from 64.3% to 65.4%. Regression results revealed that counties with high racial/ethnic residential segregation were more likely to have a low viral suppression rate (ß = -0.56, 95% CI: -0.75 to -0.37). In counties with high levels of community health, the impact of racial/ethnic residential segregation on viral suppression rate decreased as compared with those with low levels of community health (ß = 5.50, 95% CI: 0.95-10.05). Conclusions: Racial/ethnic residential segregation acts as a structural barrier to placed-based viral suppression rates and compromises the goal of the HIV treatment cascade. Concentrated and sustained county-level interventions aiming to improve community health can be practical approaches to promote health equity in HIV treatment and care.

Inspiration from the research advances in microcirculatory dysfunction to the treatment of burn shock and burn septic shock / 中华烧伤杂志

Microcirculatory dysfunction is an important pathophysiological change of shock. In the last decade, many researches on the mechanism of microcirculatory dysfunction have been involved in areas such as the glycocalyx damage of vascular endothelial cells, macrocirculation- microcirculation discoupling, vascular hyporeactivity, and microcirculation monitoring. Accordingly, this paper discussed how these research findings can be applied to burn patients, with the aim of alerting the clinicians to improving microcirculation, and maintaining hemodynamic coordination during the treatment of burn shock and burn septic shock. In addition, with the development of accurate and reliable microcirculation monitoring techniques, it is necessary to carry out multi-center clinical trials to reveal the clinical significance of target-oriented shock resuscitation protocol combining macrocirculatory and microcirculatory parameters.

Controlling excessive fluid resuscitation in massive burn patients to prevent complications / 中华烧伤杂志

Excessive fluid resuscitation in massive burn patients is a common phenomenon in burn management, and the reasons are mostly related with administering resuscitation of crystalloid alone and pursuing a goal-directed resuscitation with targeting normal hemodynamic parameters in the first 24 h post burn. Tissue edema caused by excessive fluid resuscitation is a vital factor that induces complications including respiratory compromise, abdominal compartment syndrome, and so on. Therefore, in order to control excessive fluid resuscitation and prevent its subsequent complications in massive burn patients, it is necessary to determine the optimal resuscitation regime, set appropriate resuscitation endpoints, and implement precise management of fluid resuscitation.

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility.

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

Measuring global multi-scale place connectivity using geotagged social media data.

Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. The multi-scale PCI, demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10% penetration in the US population) and Facebook's social connectedness index (SCI), a popular connectivity index based on social networks. We found that PCI has a strong boundary effect and that it generally follows the distance decay, although this force is weaker in more urbanized counties with a denser population. Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications: (1) modeling the spatial spread of COVID-19 during the early stage of the pandemic and (2) modeling hurricane evacuation destination choice. The methodological and contextual knowledge of PCI, together with the open-sourced PCI datasets at various geographic levels, are expected to support research fields requiring knowledge in human spatial interactions.

Cotreatment with Aspirin and Azole Drugs Increases Sensitivity of Candida albicans in vitro.

PURPOSE: This study aimed to investigate the effects of aspirin (acetyl salicylic acid [ASA]) combined with fluconazole (FCA), itraconazole (ITR), or voriconazole (VRC) on Candida albicans under planktonic and biofilm conditions. METHODS: A total of 39 clinical C. albicans strains were used to perform the in vitro drug sensitivity assay under different conditions using the M27-A4 broth microdilution method. The minimal inhibitory concentrations (MICs) and fractional inhibitory concentration index (FICI) values were calculated. C. albicans ZY23 was chosen for the further analyses. RESULTS: Under planktonic conditions, the half maximal MIC (MIC50) values of FCA, ITR, and VRC were 64-0.5 µg/mL, 32-0.0625 µg/mL, and 16-0.125 µg/mL, respectively, when applied, whereas in combination with ASA, the values decreased to 32-0.25 µg/mL, 8-0.0313 µg/mL, and 8-0.0313 µg/mL, respectively. Under biofilm conditions, FCA, ITR, or VRC alone showed MIC50 values of 128-8 µg/mL, 32-4 µg/mL, and 32-0.5 µg/mL, whereas in combination with ASA the values were decreased to 32-0.5 µg/mL, 16-0.5 µg/mL, and 8-0.0625 µg/mL, respectively. Analysis of the FICI showed that the sensitization rate of ASA to FCA, ITR, and FCA under planktonic conditions was 43.59%, whereas the sensitization rates of ASP to FCA, ITR, and FCA under biofilm conditions were 46.15%, 46.15%, and 48.72%, respectively. Additionally, the time-growth and time-kill curves of C. albicans ZY23 further verified the synergistic effects of ASA on azole drugs. CONCLUSION: ASA may act as an enhancer of the inhibitory effects of azole drugs on the growth of clinical C. albicans under planktonic and biofilm conditions.

Measuring Global Multi-Scale Place Connectivity using Geotagged Social Media Data.

Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. The multi-scale PCI, demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10% penetration in the US population) and Facebook's social connectedness index (SCI), a popular connectivity index based on social networks. We found that PCI has a strong boundary effect and that it generally follows the distance decay, although this force is weaker in more urbanized counties with a denser population. Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications: 1) modeling the spatial spread of COVID-19 during the early stage of the pandemic and 2) modeling hurricane evacuation destination choice. The methodological and contextual knowledge of PCI, together with the launched visualization platform and open-sourced PCI datasets at various geographic levels, are expected to support research fields requiring knowledge in human spatial interactions.

Genome sequencing and functional annotation of Bacillus sp. strain BS-Z15 isolated from cotton rhizosphere soil having antagonistic activity against Verticillium dahliae.

In the present study, antagonistic activity of bacterial strain BS-Z15, was evaluated against Verticillium dahlia. The fermented broth of BS-Z15 inhibited the growth of Verticillium dahliae. The genome of strain BS-Z15 had a total size of 4,068,702 base pairs and contained 4318 genes, of which 4196 are coding sequences and 122 are non-coding RNA. Among these genes, nine genomic islands, 86 tRNAs, 13 sRNAs, and one prophage was determined. With the help of annotation databases, most unigene functions were identified. At the same time, genomic comparison between BS-Z15 and 12 Bacillus members showed that the genes of BS-Z15 were closely related to the Bacillus group, and were conserved between the two groups, including most of the genes associated with fungal antagonism. BS-Z15 contains genes involved in a variety of antagonistic mechanisms, including genes encoding or synthesizing mycosubtilin, chitinases (but not CHIA and CHIB), glycoside hydrolases, iron nutrients, and antibiosis. However, it only contained the complete mycosubtilin- and bacilibactin-related operators in the reported main antifungal gene cluster of B. subtilis. Mycosubtilin and bacilibactin may be the main active antifungal substance. Besides, some genes could encode products related to biofilm production, which may be related to the colonization ability of the strain in plant rhizospheres. The complete genome of B. subtilis BS-Z15 provided new insights into the potential metabolites it produces related to its biocontrol activity.

Aspirin and verapamil increase the sensitivity of Candida albicans to caspofungin under planktonic and biofilm conditions.

OBJECTIVES: This study aimed to investigate the effects of caspofungin (CAS) combined with aspirin (ASP) or verapamil (VPL) on the sensitivity of Candida albicans under planktonic and biofilm conditions. METHODS: A total of 39 C. albicans clinical strains were used to construct biofilms. Sensitivity to ASP or VPL combined with CAS was analysed by broth microdilution. MIC50 values were obtained and the fractional inhibitory concentration index (FICI) was calculated. Subsequently, C. albicans ZY22 was selected for time-growth curve analysis and strains ZY15 and ZY22 were used for time-kill curve analysis. RESULTS: Under planktonic condition the MIC50 of CAS was 0.0313-8 µg/mL following treatment with CAS alone, whereas it decreased to 0.0313-4 µg/mL following CAS combined with ASP or VPL. Under biofilm condition the MIC50 of CAS was 0.125-16 µg/mL following treatment with CAS alone, whereas it decreased to 0.0625-16 µg/mL or 0.0625-8 µg/mL following CAS combined with ASP or VPL. FICI results showed synergistic interactions between CAS and ASP under planktonic and biofilm conditions in 17 and 16 strains, respectively. However, synergistic interactions between CAS and VPL under planktonic and biofilm conditions were observed in 19 and 23 strains, respectively. Additionally, 8000 µg/mL ASP or 8 µg/mL VPL combined with CAS had better inhibitory effects on C. albicans. CONCLUSION: ASP and VPL may be a sensitiser for CAS, and the antifungal effects of CAS may be sensitised by 8000 µg/mL ASP or 8 µg/mL VPL against C. albicans under planktonic and biofilm conditions.