Spatial distribution of zero-dose children in Ethiopia: evidence for a targeted intervention from a large-scale cross-sectional evaluation survey

Background

Ethiopia is the fourth leading contributor to the global total of zero-dose children (those who lack the first dose of diphtheria-tetanus-pertussis containing vaccine) and has substantial regional variations in zero-dose children. This study explored the spatial pattern of zero-dose children aged 12-35 months in Ethiopia.

Methods

A survey was conducted in pastoralist regions, developing regions, newly-established regions, conflict-affected areas, underserved urban populations, hard-to-reach areas, internally displaced populations, and refugees. Spatial autocorrelation was measured using the Global Moran'sIstatistic. Getis-Ord Gi* statistics was applied to calculate the spatial variability of the high and low prevalence rates of zero-dose children. The spatial interpolation technique was also applied to estimate unknown values that fall between known values. Inverse distance weighting interpolation method was used to predict the risk of zero-dose children. ArcGIS version 10.8 was used for the spatial analysis.

Results

A total of 3,646 children aged 12-35 months were included in the study. The spatial distribution of zero-dose children in Ethiopia was non-random (Global Moran'sI = 0.178971, p < 0.001). According to the hotspot analysis, western, eastern and northern parts of Somali and western and central parts of Afar regions had the highest load of zero-dose children (hotspot areas) followed by the Northeastern part of Amhara and southeastern part of Oromia regions. On the other hand, Southern Nations, Nationalities, and Peoples, Sidama, and the Eastern part of the Southwest Ethiopia peoples regions were identified as cold spot areas. The spatial interpolation analysis corresponded with the hotspot analysis results where western and central parts of Afar and western, eastern and northern parts of Somali regions were identified as high-risk areas for zero-dose children. However, Addis Ababa, Dire Dawa, Harari, Southern Nations, Nationalities, and Peoples, Sidama, Southwest Ethiopia Peoples, and parts of Oromia were found to be low-risk areas for zero-dose children.

Conclusion

The spatial analysis identified that zero-dose children had a significant spatial variation across the study areas. High clusters of zero-dose children were detected in Afar and Somali regions. Implementing routine and mop-up vaccination campaigns in the identified hotspot areas will help Ethiopia to improve coverage and reduce immunization inequalities.

Determination of the Spatial Distribution of Air Pollutants in Bucheon, Republic of Korea, in Winter Using a GIS-Based Mobile Laboratory

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emission sources. In Republic of Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m3 in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM2.5 concentration was 161.7 µg/m3. The maximum NOx, NO2, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.

Spatio-temporal variations of ecosystem service value and its spatial heterogeneity mechanism in the Dongjiang River Basin, China

Comprehensive and deep research on the variations of ecosystem service value (ESV) based on land utilization/land cover change from a spatio-temporal perspective is of great significance for regional ecosystem optimization, as well as coordinated sustainable development of natural environment and economic society. Based on land utilization, natural environment, and socio-economic data of Dongjiang River Basin from 2010 to 2020, combined with hotspot analysis tools and local spatial autocorrelation analysis methods, we comprehensively analyzed the spatio-temporal variations of Dongjiang River Basin ESV, and further explored the spatial differentiation mechanism with geographic detector tools. The results showed that Dongjiang River Basin was dominated by forest ecosystem from 2010 to 2020. The construction land area had expanded significantly mainly from arable land and forest. The Dongjiang River Basin ESV showed a downward and then an upward trend. The ESV of arable land, forest and construction land continuously decreased, and the ESV of water decreased first and then increased substantially. The spatial distribution of ESV hot and cold spots had a significant agglomeration effect, presenting a pattern of hot spot dispersion in the upstream area and cold spots aggregation in the downstream area. The ESV distribution in the upstream and downstream area was not balanced, with the downstream area bearing greater ecological stress. According to the detection results of ESV spatial differentiation mechanism, land utilization was the main factor affecting the spatial differentiation, with the spatial difference of ESV (q value) reaching 0.462. The interaction of factors could greatly strengthen the spatial differentiation effect on Dongjiang River Basin ESV.

Annual effective dose estimation of radon in drinking water sources of Nizampur basin, North Western Pakistan

This study assessed radon (222Rn) levels in drinking water sources in the Nizampur basin and their potential health risks for the local community. We analyzed 48 water samples on-site using RAD7. Additionally, we measured pH, temperature (T), total dissolved solids (TDS), redox potential (ORP), and electrical conductivity (EC) with a multiparameter analyzer. Results showed pH, T, TDS, ORP, and EC ranging from 7.2 to 8, 17 to 26 °C, 333 to 1130 mg/l, -56 to 284 mV, and 469 to 2370 µS/cm. 222Rn levels varied significantly (0.7-107 Bq/l, mean 23 ± 21, median = 17 Bq/l), with about 65 % exceeding the EPA's limit of 11.1 Bq/l, indicating health risks likely due to local geological conditions. The annual effective doses for ingestion (EwIng) were 0.87 ± 0.01, 0.35 ± 0.006, and 0.13 ± 0.002 µSv/a for adults, infants, and children, respectively. Exposure risk via the inhalation (EwInh) route ranged from 1.75 to 270 µSv/a, with the highest risk in infants, followed by children and adults. Inhalation was the primary exposure route for all age groups. Further, spatial distribution maps and hotspot analysis suggested that the central region characterized by high structural deformation and favorable geology for radon emanation was the area of concern in terms of health risks.

[CiteSpace knowledge map of research hotspots and frontiers of traditional Chinese medicine intervention in psoriasis in recent ten years]

This study aimed to investigate the development status of traditional Chinese medicine(TCM) intervention in psoriasis in recent ten years, analyze the research hotspots, and summarize the development trends to provide reference materials for scholars in this field. Taking the available literature related to the field of TCM intervention in psoriasis as the research object, the trends, contents, and source publications were statistically analyzed based on bibliometrics. The research cooperation and co-occurrence of keywords in this field were studied by the knowledge map analysis method based on CiteSpace. The total number of Chinese papers was 2 993 and English papers 285. In terms of publication trend, the annual publication of English papers was low but showed an obvious upward trend, while the increase in Chinese papers fluctuated and tended to be flat. In terms of the content of Chinese papers published, TCM ranked first according to the discipline(2 415). In English papers, the number of publications in pharmacology and pharmaceutical science was the highest(87). Literature source analysis showed that the Chinese and English journals with the most publications were China Journal of Traditional Chinese Medicine and Pharmacy and Evidence Based Complementary and Alternative Medicine, respectively. Beijing University of Chinese Medicine published the most dissertations in China(99). The authors with the most publications in Chinese and English were LI Bin(Yueyang Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Shanghai University of Traditional Chinese Medicine) and LU Chuan-jian(Guangdong Hospital of Traditional Chinese Medicine). As revealed by the CiteSpace analysis of the research cooperation network, there were four mature and stable core teams in this field, but the cooperation intensity between different teams was weak. According to the keywords co-occurrence knowledge graph constructed by CiteSpace, the current hot keywords in this field are as follows: psoriasis, blood-heat syndrome, blood-stasis syndrome, fire needle, blood-dryness type, imiquimod, TCM bath, etiology and pathogenesis, cytokines, cupping therapy, etc. In summary, Chinese scholars have conducted active exploration and research in the field of TCM intervention in psoriasis in recent ten years. The overall development trend is good, and the breadth and depth of the research are constantly extending. It is suggested that relevant research should be free from discipline restrictions and strive for interdisciplinary integration.

Assessment of Multiple Ecosystem Services and Ecological Security Pattern in Shanxi Province, China

The ecological security pattern construction could effectively regulate ecological processes and ensure ecological functions, then rationally allocate natural resources and green infrastructure, and, finally, realize ecological security. In view of serious soil erosion, accelerated land desertification, soil pollution and habitat degradation in Shanxi Province, the spatial distribution of six key ecosystem services, including water conservation (WC), soil conservation (SC), sand fixation (SF), carbon storage (CS), net primary productivity (NPP) and habitat quality (HQ), was analyzed by using multiple models. The comprehensive ability of multiple ecosystem services in different regions was quantified by calculating multiple ecosystem services landscape index (MESLI). Combined with ecosystem services hotspots, the ecological security pattern of Shanxi Province was constructed by using the minimum cumulative resistance model. The results showed that the spatial differences in ecosystem services in Shanxi Province were obvious, which was low in the seven major basins and Fen River valley, and high in the mountains (especially Taihang and Lvliang Mountains) for WC, SC, CS, NPP and HQ, while high SF was only distributed in the northern Shanxi. The MESLI showed that the ability to provide multiple ecosystem services simultaneously was low in Shanxi Province, with the medium and low grade MESLI regions accounting for 58.61%, and only 18.07% for the high grade MESLI regions. The important protected areas and ecological sources of the ecological security pattern were concentrated in the Lvliang and Taihang Mountains, which were consistent with the key areas of ecosystem services. The ecological corridors illustrated network distribution with ecological sources as the center, the low-, medium- and high-level buffers accounted for 26.34%, 17.03% and 16.35%, respectively. The results will provide important implications for economic transformation, high-quality development and ecological sustainable development in resource-based regions worldwide.

Investigating the Spatial Relationship between Social Vulnerability and Healthcare Facility Distribution in Nassau County, New York

Health is a fundamental human right, yet healthcare facilities are not distributed equitably across all communities. This study aims to investigate the distribution of healthcare facilities in Nassau County, New York, and examine whether the distribution is equitable across different social vulnerability levels. An optimized hotspot analysis was conducted on a dataset of 1695 healthcare facilities-dental, dialysis, ophthalmic, and urgent care-in Nassau County, and social vulnerability was measured using the FPIS codes. The study found that healthcare facilities were disproportionately distributed in the county, with a higher concentration in areas of low social vulnerability compared to areas of high social vulnerability. The majority of healthcare facilities were found to be clustered in two ZIP codes-11020 and 11030-that rank among the top ten wealthiest in the county. The results of this study suggest that socially vulnerable residents in Nassau County are at a disadvantage when it comes to attaining equitable access to healthcare facilities. The distribution pattern highlights the need for interventions to improve access to care for marginalized communities and to address the underlying determinants of healthcare facility segregation in the county.

Built-environment risk assessment for pedestrians near bus-stops: a case study in Delhi

Pedestrian safety is a serious concern in the developing nations of the world. It is evident from the past studies that built-environment characteristics near bus-stops, play a crucial role on the frequency and overall share of pedestrian deaths and injuries in road traffic crashes. The present study aims to identify critical built-environment features around vulnerable bus-stops in an Indian city and evaluate the odds of risk that prevails on the safety of pedestrians near bus stops. Hotspot analysis was conducted to finalise 177 bus stop sites within high-crash clusters in the study area. Built-environment attributes considered were based on sidewalk, crosswalk and bus stop conditions near such vulnerable locations. This study includes a video graphic and manual field survey conducted during the day and night-time. Logistic regression was applied to estimate the impact of built environment features on pedestrian crashes. Width and disability friendliness of sidewalks, presence of bus bays and on-street parking have significant impacts on pedestrian fatalities at locations with a higher share of pedestrian fatalities during the day. On the other hand, presence of zebra crossings at junctions, proper bus stop lighting and high sidewalks reduce the odds of pedestrian crashes at night near bus stops.

The Link between Landscape Characteristics and Soil Losses Rates over a Range of Spatiotemporal Scales: Hubei Province, China

Controlling soil erosion is beneficial to the conservation of soil resources and ecological restoration. Understanding the spatial distribution characteristics of soil erosion helps find the key areas for soil control projects and optimal scale for investing in a soil and water conservation project at the lowest cost. This study aims to answer the question of how the spatial distribution of soil erosion in Hubei Province changed between 2000 and 2020. Moreover, how do the effects of natural factors and human activities on soil erosion vary over the years? What are the differences in landscape pattern characteristics and the spatial cluster of soil erosion at multiple administrative scales? We simulated the spatial distribution of soil erosion in Hubei province from 2000 to 2020 by the Chinese Soil Loss Equation model at three administrative scales. We investigated the relationship between soil erosion and driving factors by Geodector. We explored the landscape pattern and hotspots of land at different levels of soil erosion by Fragstat and hotspot analysis. The results show that: (1) The average soil erosion rate decreased from 2000 to 2020. Soil erosion is severe in the mountainous areas of western Hubei province, while it is less severe in the central plains. (2) Land-cover type, precipitation, and normalized difference vegetation index are the most influencing factors of soil erosion in 2000–2010, 2015, and 2020, respectively. (3) The aggregation index values at the town scale are higher than those at the city and county scales, while the fractal dimension index values at the town scale are lower, which indicates that soil erosion projects are most efficient when the project unit is ‘town’. (4) At the town scale, if the hotspot area (6.84% of the total area) is treated as the protection target, it can reduce 50.42% of the total soil erosion of Hubei province. Hotspots of soil erosion overlap with high erosion zones, mainly in the northwestern, northeastern, and southwestern parts of Hubei province in 2000, while the hotspots in northwestern Hubei disappear in 2020. In conclusion, land managers in Hubei should optimize the land-use structure, soil and water conservation in slope land, and eco-engineering controls at the town scale.

[Land use change and its driving force on the southern slope of Qilian Mountains from 1980 to 2018]

Qilian Mountains is the boundary of the first and second steps of China's terrain, with fragile ecological environment. There is great ecological significance to study land use change and driving force in transitional areas. In this study, we investigated the spatial and temporal characte-ristic of land use and its driving force in the south slope of Qilian Mountains, based on RS image data from 1980 to 2018, with the spatial autocorrelation method, ArcGIS spatial analysis method and principal component analysis. The results showed that, from 1980 to 2018, grassland was the main land use type, and the proportion of construction land was the smallest. Water area and grassland showed a declining trend, while unused land, construction land and farmland showed an increasing trend. There was smaller change for the woodland. The single dynamic degree of different land types decreased following an order of construction land, water, farmland, unused land, woodland and grassland. The comprehensive dynamic degree of land use was 0.9%. The spatial distribution of different land use types showed the characteristics of spatial agglomeration. The increased areas of farmland and the decreased areas of woodland and grassland were mainly distributed in the northwest of the Datong River valley of Menyuan County, while in the upper reaches of Datong River in the northeast of Tianjun County, grassland was occupied by construction land. The driving force of land use was population, science and technology, urbanization, level of economic development, and policies. Our results would support the government to reasonably plan and utilize land resources, which is of significance to the protection of ecological environment and the sustainable development of society and economy on the southern slope of Qilian Mountains.

Spatial variation and disparity in female breast cancer relative survival in the United States

BACKGROUND

Breast cancer is the most common cancer among women in the United States. However, data on spatial disparities in survival for breast cancer are limited in the country. This study estimated 5-year relative survival (RS) of female breast cancer and examined the spatial variations across the contiguous United States.

METHODS

Women newly diagnosed with breast cancer in 2003-2010 in the United States were identified from the National Cancer Database and followed up through 2016. The crude 5-year RS at the county level was estimated and adjusted for patients' key sociodemographic and clinical factors. To account for spatial effects, the RS estimates were smoothed using the Bayesian spatial survival model. A local spatial autocorrelation analysis with the Getis-Ord Gi* statistics was applied to identify geographic clusters of low or high RS.

RESULTS

Clusters of low RS were identified in more than 15 states covering 671 counties, mostly in the southeast and southwest regions, including Georgia, Alabama, Mississippi, Louisiana, Arkansas, Oklahoma, and Texas. Approximately 30% of these clusters can be explained by patients' characteristics: Race, insurance, and stage at diagnosis appeared to be the major attributable factors.

CONCLUSIONS

Significant spatial disparity in female breast cancer survival was found, with low RS clusters identified in Georgia, Alabama, Mississippi, Louisiana, Arkansas, Oklahoma, and Texas. Policies and interventions that focus on serving Black women, improvements in insurance coverage, and early detection in these areas could potentially mitigate the spatial disparities.

Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique

The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value < 0.01). The result of Getis-Ord Gi* statistics revealed that the total population and reported COVID-19 cases have got high priority for considering hotspot with greater z-score (> 3 and > 0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19.

[Spatial-temporal variation in hotspot and coldspot of Antarctic krill distribution in recent 100 years]

To analyze the long-term variation in the spatial-temporal patterns of Antarctic krill (Euphausia superba) distribution, based on krill density data collected from 1926 to 2016, we analyzed the spatial and temporal variation of krill using the Getis-Ord G_i^* statistic and the Triangulated Irregular Network (TIN) model. We compared the krill abundance, the percentage of krill abundance, and the size of hotspot/coldspot area. The results showed that there were one core hot-spot area, one sub-hotspot area and one edge hotspot area from 1926 to 1935 and from 1936 to 1945; one core hotspot area, one sub-hotspot area and one edge hotspot/coldspot area from 1976 to 1985; two core hotspot areas, two sub-hotspot areas, two edge hotspot areas, one sub-coldspot area and one coldspot area from 1986 to 1995; two core hotspot areas, two sub-hotspot/sub-coldspot areas and two edge hotspot/coldspot areas from 1996 to 2005; and from 2006 to 2016, there were two sub-hotspot areas, two edge hotspot areas, one sub-coldspot area and one edge coldspot area, respectively. Those hotspots/coldspots mostly occurred in the South Sandwich Islands, the Antarctic Peninsula, and the west of the Prydz Bay. From 1926 to 2016, krill abundance, the percentage of krill abundance in the hotspot area and the size of hotspot area had generally reduced, but the percentage of krill abundance in coldspot area and the size of coldspot area were increasing, while krill abundance in coldspot area decreased. It is a feasible approach to build an irregular triangle network to measure the size of hotspot/coldspot area and utilize hotspot analysis with 95% confidence to analyze the spatial-temporal pattern of krill distribution.

Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?

Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.

Spatiotemporal Variation and Hotspot Detection of the Avian Influenza A(H7N9) Virus in China, 2013⁻2017

This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from 19 February 2013 to 30 September 2017 extracted from Centre for Health Protection of the Department of Health (CHP) and electronic databases managed by China's Center for Disease Control (CDC) and provincial CDCs synthetically using the Geographic Information System (GIS) software ArcMap™ 10.2 and SaTScan. Based on the multiple analyses of the A(H7N9) epidemics, there was a strong seasonal pattern in A(H7N9) virus infection, with high activity in the first quarter of the year, especially in January, February, and April, and a gradual dying out in the third quarter. Spatial distribution analysis indicated that Eastern China contained the most severely affected areas, such as Zhejiang Province, and the distribution shifted from coastline areas to more inland areas over time. In addition, the cases exhibited local spatial aggregation, with high-risk areas most found in the southeast coastal regions of China. Shanghai, Jiangsu, Zhejiang, and Guangdong were the high-risk epidemic areas, which should arouse the attention of local governments. A strong cluster from 9 April 2017 to 24 June 2017 was also identified in Northern China, and there were many secondary clusters in Eastern and Southern China, especially in Zhejiang, Fujian, Jiangsu, and Guangdong Provinces. Our results suggested that the spatial-temporal clustering of H7N9 in China is fundamentally different, and is expected to contribute to accumulating knowledge on the changing temporal patterns and spatial dissemination during the fifth epidemic and provide data to enable adequate preparation against the next epidemic.

[Inter-decadal variation of spatial aggregation of Trichiurus japonicus in East China Sea based on spatial autocorrelation analysis]

Marine fish shows high heterogeneity in spatial aggregation. We analyzed the inter-deca-dal variations of stock density for Trichiurus japonicus in East China Sea (ECS) using geo-statistical approaches such as spatial autocorrelation and hotspot analysis, based on the data of T. japonicus from both bottom trawl fishery and research surveys in the open waters of ECS during 1971 to 2011, combined with the sea surface temperature (SST) and surface salinity data in the PN section in August. The global spatial autocorrelation statistics showed that Moran's I firstly decreased and then went up, indicating that the spatial aggregation patterns of T. japonicus was weakened in the beginning and then increased during 1971 to 2011. The surface salinity in the PN section displayed the opposite trend during the same period. The local spatial autocorrelation statistics showed that the population firstly moved to the southern ECS and then to the northern ECS except in 1971 in which the population concentrated in the middle of ECS because of the restriction of offshore fishing ground. The movement of hotspot areas of T. japonicus adaptively varied with the first EOF mode of SST in summer (sumEOF1), which indicated that the hotspot areas first moved southeastward with decreasing sumEOF1, and moved northeastward with increasing sumEOF1, but all of the hotspot areas were close to the northward branch of the Kuroshio Current.

Systematic characterization of pan-cancer mutation clusters

Cancer genome sequencing has shown that driver genes can often be distinguished not only by the elevated mutation frequency but also by specific nucleotide positions that accumulate changes at a high rate. However, properties associated with a residue's potential to drive tumorigenesis when mutated have not yet been systematically investigated. Here, using a novel methodological approach, we identify and characterize a compendium of 180 hotspot residues within 160 human proteins which occur with a significant frequency and are likely to have functionally relevant impact. We find that such mutations (i) are more prominent in proteins that can exist in the on and off state, (ii) reflect the identity of a tumor of origin, and (iii) often localize within interfaces which mediate interactions with other proteins or ligands. Following, we further examine structural data for human protein complexes and identify a number of additional protein interfaces that accumulate cancer mutations at a high rate. Jointly, these analyses suggest that disruption and dysregulation of protein interactions can be instrumental in switching functions of cancer proteins and activating downstream changes.

Identification of Single Nucleotide Non-coding Driver Mutations in Cancer

Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization.

Assessing Lightning and Wildfire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining in Alberta, Canada

Hotspot analysis was implemented to find regions in the province of Alberta (Canada) with high frequency Cloud to Ground (CG) lightning strikes clustered together. Generally, hotspot regions are located in the central, central east, and south central regions of the study region. About 94% of annual lightning occurred during warm months (June to August) and the daily lightning frequency was influenced by the diurnal heating cycle. The association rule mining technique was used to investigate frequent CG lightning patterns, which were verified by similarity measurement to check the patterns' consistency. The similarity coefficient values indicated that there were high correlations throughout the entire study period. Most wildfires (about 93%) in Alberta occurred in forests, wetland forests, and wetland shrub areas. It was also found that lightning and wildfires occur in two distinct areas: frequent wildfire regions with a high frequency of lightning, and frequent wild-fire regions with a low frequency of lightning. Further, the preference index (PI) revealed locations where the wildfires occurred more frequently than in other class regions. The wildfire hazard area was estimated with the CG lightning hazard map and specific land use types.

Climate change will increase the naturalization risk from garden plants in Europe

AIM

Plant invasions often follow initial introduction with a considerable delay. The current non-native flora of a region may hence contain species that are not yet naturalized but may become so in the future, especially if climate change lifts limitations on species spread. In Europe, non-native garden plants represent a huge pool of potential future invaders. Here, we evaluate the naturalization risk from this species pool and how it may change under a warmer climate.

LOCATION

Europe.

METHODS

We selected all species naturalized anywhere in the world but not yet in Europe from the set of non-native European garden plants. For this subset of 783 species, we used species distribution models to assess their potential European ranges under different scenarios of climate change. Moreover, we defined geographical hotspots of naturalization risk from those species by combining projections of climatic suitability with maps of the area available for ornamental plant cultivation.

RESULTS

Under current climate, 165 species would already find suitable conditions in > 5% of Europe. Although climate change substantially increases the potential range of many species, there are also some that are predicted to lose climatically suitable area under a changing climate, particularly species native to boreal and Mediterranean biomes. Overall, hotspots of naturalization risk defined by climatic suitability alone, or by a combination of climatic suitability and appropriate land cover, are projected to increase by up to 102% or 64%, respectively.

MAIN CONCLUSIONS

Our results suggest that the risk of naturalization of European garden plants will increase with warming climate, and thus it is very likely that the risk of negative impacts from invasion by these plants will also grow. It is therefore crucial to increase awareness of the possibility of biological invasions among horticulturalists, particularly in the face of a warming climate.

Spatial patterns of off-the-system traffic crashes in Miami-Dade County, Florida, during 2005-2010

OBJECTIVE

The objective of this study is to analyze the spatial distribution of the vehicles involved in crashes in Miami-Dade County. In addition, we analyzed the role of time of day, day of the week, seasonality, drivers' age in the distribution of traffic crashes.

METHOD

Off-the-system crash data acquired from the Florida Department of Transportation during 2005-2010 were divided into subcategories according to the risk factors age, time of day, day of the week, and travel season. Various spatial statistics methods, including nearest neighbor analysis, Getis-Ord hot spot analysis, and kernel density analysis revealed substantial spatial variations, depending on the subcategory in question.

RESULTS

Downtown Miami and South Beach showed up consistently as hotspots of traffic crashes in all subcategories except fatal crashes. However, fatal crashes were concentrated in residential areas in inland areas.

CONCLUSION

This understanding of patterns can help the county target high-risk areas and help to reduce crash fatalities to create a safer environment for motorists and pedestrians.

[Spatial Characteristics and Environmental Risk of Heavy Metals in Typical Gold Mining Area of Shandong Province]

In order to reveal the influence of anthropogenic factors on soil environment quality, a total of seventy-seven samples in topsoils were collected from Jiaojia gold mining area in Shandong province and were determined for Cu, Pb, Zn, Cr contents. Spatial structure, spatial distributions of concentrations and risk probability of heavy metals were analyzed using spatial statistic analysis. The average concentrations of Cu, Pb, Zn and Cr were 19.41 mg·kg^-1, 27.32 mg·kg^-1, 49.81 mg·kg^-1 and 39.27 mg·kg^-1, respectively. Pb, Zn and Cr were distributed normally and Cu was distributed normally after logarithm transformation. Semivariance analysis demostrated that Pb could be fitted to exponential model, and Cu, Zn and Cr were fit for spherical model. Nugget coefficents of Cu and Pb were between 0.25 and 0.75, which illustrated middle spatial autocorrelation; Zn and Cr showed the structural variation with nugget values below 0.25. Cu and Pb in the topsoils were distributed dispersedly due to effects of some human factors, whereas contents of Zn and Cr indicated relatively regular distributions and were mainly affected by natural factors. Spatial distributions of the 4 heavy metals were approximately consisitent and the high value areas appeared in the gold mines band. The result of hot spot analysis and indicator kriging interpolation revealed that the relatively high risk areas were located in Jincheng town, the boundary zone of Xinzhuang town and Canzhuang town, while the safe zone was situated in south part of the study area. Pb had higher probability exceeding the threshold and the middle or high environmental risk areas of Pb were distributed widely, which should be paid more attentions.

Ranging and site fidelity in northern pigtailed macaques (Macaca leonina) over different temporal scales

Space-use patterns are crucial to understanding the ecology, evolution, and conservation of primates, but detailed ranging data are scarce for many species, especially those in Southeast Asia. Researchers studying site fidelity to either home ranges or core areas have focused mainly on territorial species, whereas less information is available for non-territorial species. We analyzed the ranging patterns and site fidelity of one wild troop of northern pigtailed macaques over 16 months at different temporal scales. We used characteristic hull polygons in combination with spatial statistics to estimate home ranges and core areas. The total home range and core areas were 449 ha and 190 ha, respectively. Average daily path length was 2,246 m. The macaques showed a high defendabili--ty index according to the expected ranging of a non-territorial species in which movement does not theoretically permit the defense of a large territory. Overall, the study troop ranged more extensively than conspecific groups and closely related species studied elsewhere. These differences may reflect variable troop size, degree of terrestriality and habitat characteristics, but could also reflect methodological differences. The location, size and shape of home ranges and core areas, and extent of daily path lengths changed on a monthly basis resulting in low site fidelity between months. The macaques also showed clear shifts in the location of daily home ranges with low site fidelity scores between consecutive days. Daily home range and daily path length were related to seasonality, with greater values during the fruit-abundant period. Low site fidelity associated with lack of territoriality is consistent with macaques structuring their movement based on available food sources. However, ranging patterns and site fidelity can also be explained by macaques feeding on the move, a foraging strategy that hinders frequent and long visits to the same location.

Examining the spatial distribution of law enforcement encounters among people who inject drugs after implementation of Mexico's drug policy reform

In 2009, Mexico decriminalized the possession of small amounts of illicit drugs for personal use in order to refocus law enforcement resources on drug dealers and traffickers. This study examines the spatial distribution of law enforcement encounters reported by people who inject drugs (PWID) in Tijuana, Mexico to identify concentrated areas of policing activity after implementation of the new drug policy. Mapping the physical location of law enforcement encounters provided by PWID (n = 461) recruited through targeted sampling, we identified hotspots of extra-judicial encounters (e.g., physical/sexual abuse, syringe confiscation, and money extortion by law enforcement) and routine authorized encounters (e.g., being arrested or stopped but not arrested) using point density maps and the Getis-Ord Gi* statistic calculated at the neighborhood-level. Approximately half of the participants encountered law enforcement more than once in a calendar year and nearly one third of these encounters did not result in arrest but involved harassment or abuse by law enforcement. Statistically significant hotspots of law enforcement encounters were identified in a limited number of neighborhoods located in areas with known drug markets. At the local-level, law enforcement activities continue to target drug users despite a national drug policy that emphasizes drug treatment diversion rather than punitive enforcement. There is a need for law enforcement training and improved monitoring of policing tactics to better align policing with public health goals.

Utilizing Geographic Information Systems and Spatial Video to Analyze Patterns of Secondhand Smoke Exposure on College Campuses

OBJECTIVE

Innovatively apply geographic information system (GIS) software and spatial video cameras to examine hotspots of smoking and secondhand smoke (SHS) exposure on a college campus.

PARTICIPANTS

Pedestrians on a large public university in the United States.

METHODS

Spatial video cameras mounted on bicycles and GIS software captured and analyzed smoking on campus. Data were collected during times of heavy pedestrian traffic. Hotspot locations with high concentrations of observed smoking were identified.

RESULTS

Using this innovative methodology, several hotspots were identified on campus where SHS exposure occurs frequently. These hotspots were in areas of high pedestrian traffic, thus indicating a potential for SHS exposure to many students.

CONCLUSIONS

This paper demonstrates an innovative application of GIS and spatial video technology to understand outdoor smoking patterns on campus. These techniques can aid in program planning, decision-making, policy development, and enforcement.