Immediate visual reproduction negatively correlates with brain entropy of parahippocampal gyrus and inferior occipital gyrus in bipolar II disorder adolescents.

BACKGROUND: Brain entropy reveals complexity and irregularity of brain, and it has been proven to reflect brain complexity alteration in disease states. Previous studies found that bipolar disorder adolescents showed cognitive impairment. The relationship between complexity of brain neural activity and cognition of bipolar II disorder (BD-II) adolescents remains unclear. METHODS: Nineteen BD-II patients (14.63 ±1.57 years old) and seventeen age-gender matched healthy controls (HCs) (14.18 ± 1.51 years old) were enlisted. Entropy values of all voxels of the brain in resting-state functional MRI data were calculated and differences of them between BD-II and HC groups were evaluated. After that, correlation analyses were performed between entropy values of brain regions showing significant entropy differences and clinical indices in BD-II adolescents. RESULTS: Significant differences were found in scores of immediate visual reproduction subtest (VR-I, p = 0.003) and Stroop color-word test (SCWT-1, p = 0.015; SCWT-2, p = 0.004; SCWT-3, p = 0.003) between the two groups. Compared with HCs, BD-II adolescents showed significant increased brain entropy in right parahippocampal gyrus and right inferior occipital gyrus. Besides, significant negative correlations between brain entropy values of right parahippocampal gyrus, right inferior occipital gyrus and immediate visual reproduction subtest scores were observed in BD-II adolescents. CONCLUSIONS: The findings of the present study suggested that the disrupted function of corticolimbic system is related with cognitive abnormality of BD-II adolescents. And from the perspective temporal dynamics of brain system, the current study, brain entropy may provide available evidences for understanding the underlying neural mechanism in BD-II adolescents.

Longitudinal disparities in social determinants of health and COVID-19 incidence and mortality in the United States from the three largest waves of the pandemic.

The United States experienced at least five COVID-19 waves linked with different mutated SARS-CoV-2 variants including Alpha, Delta and Omicron. In addition to the variants, the intensity, geographical distribution, and risk factors related to those waves also vary within socio-demographic characteristics and timeframes. In this project, we have examined the spatial and temporal pattern of COVID-19 in the USA and its associations with Social Determinants of Health (SDoH) by utilizing the County Health Rankings & Roadmaps (CHRR) dataset. Our epidemiologic investigation at the county level showed that the burden of COVID-19 cases and deaths is higher in counties with high percentages of smoking, number of preventable hospital stays, primary care physician rate, the average daily density of PM2.5 and percentages of high proportions of Hispanic residents. In addition, the analysis also demonstrated that COVID-19 incidence and mortality had distinct characteristics in their association with SDoH variables. For example, the percentages of the population 65 and older had negative associations with incidence while a significant positive association with mortality. In addition to the elderly population, median household income, unemployment, and number of drug overdose deaths showed a mixed association with COVID-19 incidence and mortality. Our findings validate several influential factors found in the existing social epidemiology literature and highlight temporal associations between SDoH variables and COVID-19 incidence and mortality not yet frequently studied.

Transcriptome mapping related genes encoding PR1 protein involved in necrotic symptoms to soybean mosaic virus infection.

Necrosis caused by soybean mosaic virus (SMV) has not been specifically distinguished from susceptible symptoms. The molecular mechanism for the occurrence of necrosis is largely overlooked in soybean genetic research. Field evaluation reveals that SMV disease seriously influences soybean production as indicated by decreasing 22.4% ~ 77.0% and 8.8% ~ 17.0% of yield and quality production, respectively. To expand molecular mechanism behind necrotic reactions, transcriptomic data obtained from the asymptomatic, mosaic, and necrotic pools were assessed. Compared between asymptomatic and mosaic plants, 1689 and 1752 up- and down-regulated differentially expressed genes (DEGs) were specifically found in necrotic plants. Interestingly, the top five enriched pathways with up-regulated DEGs were highly related to the process of the stress response, whereas the top three enriched pathways with down-regulated DEGs were highly related to the process of photosynthesis, demonstrating that defense systems are extensively activated, while the photosynthesis systems were severely destroyed. Further, results of the phylogenetic tree based on gene expression pattern and an amino acid sequence and validation experiments discovered three PR1 genes, Glyma.15G062400, Glyma.15G062500, and Glyma.15G062700, which were especially expressed in necrotic leaves. Meanwhile, exogenous salicylic acid (SA) but not methyl jasmonate (MeJA) could induce the three PR1 gene expressions on healthy leaves. Contrastingly, exogenous SA obviously decreased the expression level of Glyma.15G062400, Glyma.15G062500, and concentration of SMV, but increased Glyma.15G062700 expression in necrotic leaves. These results showed that GmPR1 is associated with the development of SMV-induced necrotic symptoms in soybean. Glyma.15G062400, Glyma.15G062500, and Glyma.15G062700 is up-regulated in necrotic leaves at the transcriptional levels, which will greatly facilitate a better understanding of the mechanism behind necrosis caused by SMV disease. Supplementary information: The online version contains supplementary material available at 10.1007/s11032-022-01351-3.

Modeling crop yield using NDVI-derived VGM metrics across different climatic regions in the USA.

An accurate, cost-efficient, and early crop yield projection is a national and global necessity. This study is aimed to achieve the national demands by proposing crop yield estimation models on the Google Earth Engine (GEE) platform. This study directly used dynamic crop phenology metrics to model soybean crop yield by considering climatic regions in the USA (e.g., Central, East, Northeast, South, Southeast, and West North Central regions). We modeled soybean yields with vegetative growth metrics (VGMs) of NDVI narrated as VGM70 (avg. NDVI of 70 days from the emergence), VGM85 (avg. NDVI of 85 days from the emergence), VGM98T (total NDVI of 98 days from the emergence), VGM120 (avg. NDVI of 120 days from the emergence), VGMmean (avg. NDVI of the growth season), VGMmax (maximum NDVI of the growth season), and climatic factors (i.e., daytime surface temperature: DST, night-time surface temperature: NST, and precipitation) from 2000 to 2019. This study further explored individual predictors and a combination of predictors in modeling crop yield for diverse climatic regions. Therefore, we proposed six crop yield linear models for each of the climatic divisions, and these models are then compared with support vector machine (SVM) models. All models showed reliable predictability based on adjusted R-square, normalized root mean square error (NRMSE), normalized mean prediction error (NMPE) parameters, and a p-value of less than 0.001.The impact of the independent predictor in the best crop yield models is discussed based on the regression weights (beta weight: ß), and, the VGMmax is identified as the significant predictor in crop yield modeling for different climatic regions. Overall, this study will help the national agricultural management system for better monitoring and forecasting of soybean yield to support and manage soybean production.

A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States.

A major challenge in managing disasters during a pandemic is assessing the inequalities in society and protecting vulnerable people. The objective of this paper is to geographically understand the discrimination and inequality against minorities by COVID-19. This study designed a locational discrimination index (LDI) to measure COVID-19 discrimination against minorities at county-level in the US. LDI is the difference between the death proportion of a minority and the proportion of a minority's population. If LDI >0 is significant, COVID-19 discrimination is identified against a minority in a county. I further developed a locational minority inequality index (LMII), and LMII of a minority is directly quantified by comparing its LDI with the LDI of the majority population (i.e., the White population in the US). If LMII>0 is significant, a significant health inequality is confirmed against a minority in a county. In the US, I found 157 counties with significant discrimination against Black people, and 103 counties with significant inequality against Black people; 58 counties with significant discrimination against the American Indian population, but 38 counties with significant inequality against the American Indian population; 17 counties with significant discrimination against Native Hawaiians, but only 8 counties with significant inequality; for Hispanic people, 47 counties had significant discrimination, and 64 counties had significant inequality; for Asians, 7 counties had significant discrimination, but 28 had significant inequality. LDI, LMII, and the thematic mapping provide novel insight into COVID-19 discrimination and inequalities. To the best of our knowledge, this is the first time anyone has quantitatively and statistically defined and mapped COVID-19 discrimination and inequality against minorities at a county-level across the US. Based on this, governments and communities could make efficient decisions and take effective action to addressthe significant discrimination and inequality against Black, American Indian, Native Hawaiian, Hispanic, and Asian people, which can be applied to other pandemics or public health disasters in the USA or other countries.

Thalamo-cortical inter-subject functional correlation during movie watching across the adult lifespan.

An increasing number of studies have shown that the functional interactions between the thalamus and cerebral cortices play an important role in cognitive function and are influenced by age. Previous studies have revealed age-related changes in the thalamo-cortical system within individuals, while neglecting differences between individuals. Here, we characterized inter-subject functional correlation (ISFC) between the thalamus and several cortical brain networks in 500 healthy participants aged 18-87 years old from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) cohort using movie-watching state fMRI data. General linear models (GLM) were performed to assess age-related changes in ISFC of thalamo-cortical networks and the relationship between ISFC and fluid intelligence. We found significant age-related decreases in ISFC between the posterior thalamus (e.g., ventral posterior nucleus and pulvinar) and the attentional network, sensorimotor network, and visual network (FDR correction with p < 0.05). Meanwhile, the ISFC between the thalamus (mainly the mediodorsal nucleus and ventral thalamic nuclei) and higher-order cortical networks, including the default mode network, salience network and control network, showed complex changes with age. Furthermore, the altered ISFC of thalamo-cortical networks was positively correlated with decreased fluid intelligence (FDR correction with p < 0.05). Overall, our results provide further evidence that alterations in the functional integrity of the thalamo-cortical system might play an important role in cognitive decline during aging.

The Multiple Cooperative Mechanism and Globalization Path of Small Inland Cities in China: A Showcase Study of Dunhuang, China.

Currently, urbanization driven by global capital flows entails a main trend in many large cites in China, while global capital investment in small inland cities especially in western China is extremely scarce, where their globalization characters the powerful nationalization power and market activation. Dunhuang, a small inland city in western China, has transformed successfully from an agricultural county to an international tourist city, a platform for worldwide cultural communication, and a node city in the Belt and Road region because of its unique and brilliant resources: Mogao Grottoes and Dunhuangology. Therefore, this paper develops a conceptual framework of the multiple cooperative mechanisms and globalization path (MCMGP) of Dunhuang, elaborating the process of industrial transformation, urban globalization, and multiple cooperative mechanisms between government and market actors based on interviewing records and statistics. Findings show that the MCMGP features government-led intervention, resource orientation, and centralization that embodies the driver of state-owned enterprises (SOEs). Also, the MCM in Dunhuang's globalization contains the mechanism of enrolment, mobilization and action, governance and global marketing, distributed in the two phases. Equally important, in response to the Belt and Road Initiative (BRI) and Silk Road (Dunhuang) International Cultural Expo (SRDICE) from the state, the city government has significantly reinvested and refined cultural tourism via governance mechanisms, carving out a key node city in the Silk Road and elevating an international tourist city. Environmentally, Dunhuang's tourism internationalization enhances the process of the development of a sustainable shared mobility industry. Furthermore, its tourism development and social-ecology system maintain the synergistic relationships which international tourism promotes such as urban ecosystem and public welfare and in turn, social-ecological enhancement serve Dunhuang's international tourism well. Practical implications of how Dunhuang's experience may have lessons for others are discussed in China's peculiar socialist market economy discourse.

Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients.

The diagnosis based on clinical assessment of pediatric bipolar disorder (PBD) may sometimes lead to misdiagnosis in clinical practice. For the past several years, machine learning (ML) methods were introduced for the classification of bipolar disorder (BD), which were helpful in the diagnosis of BD. In this study, brain cortical thickness and subcortical volume of 33 PBD-I patients and 19 age-sex matched healthy controls (HCs) were extracted from the magnetic resonance imaging (MRI) data and set as features for classification. The dimensionality reduced feature subset, which was filtered by Lasso or f_classif, was sent to the six classifiers (logistic regression (LR), support vector machine (SVM), random forest classifier, naïve Bayes, k-nearest neighbor, and AdaBoost algorithm), and the classifiers were trained and tested. Among all the classifiers, the top two classifiers with the highest accuracy were LR (84.19%) and SVM (82.80%). Feature selection was performed in the six algorithms to obtain the most important variables including the right middle temporal gyrus and bilateral pallidum, which is consistent with structural and functional anomalous changes in these brain regions in PBD patients. These findings take the computer-aided diagnosis of BD a step forward.

Controlled-release urea combined with fulvic acid enhanced carbon/nitrogen metabolic processes and maize growth.

BACKGROUND: Controlled-release urea (CRU) or fulvic acid (FA), when applied, have been shown to increase nitrogen (N) use efficiency (NUE) or to stimulate plant growth, yet their interactive effects are not well explored. The objective of this study was to investigate the synergistic mechanisms of CRU combined with FA (CRU + FA) on maize (Zea mays L.) growth. Through the experimental design with five treatments, the N metabolism through the transcriptomic analysis of maize leaf, endogenous hormones, photosynthesis enzymes in maize leaf and root, and maize yield and NUE were evaluated. RESULTS: Compared with CRU treatment, CRU + FA treatment significantly increased auxin, nitrate reductase, and glutamate dehydrogenase in leaf by 35.4%, 43.9%, 40.8% and 19.5%, respectively, as well as, the relative content of the leaf chlorophyll and photosynthetic rate by 14.8% and 45.6%, respectively, at 12-leaf collar stage; the carbon/nitrogen (C/N) metabolic process was significantly enriched in CRU + FA treatment by 312 and 418 genes, according to transcriptome profiles of C/N metabolic in leaves from various fertilizer treated maize; maize yield and NUE of CRU + FA treatment were increased by 6.3% and 38.4%, respectively. CONCLUSIONS: These results demonstrated that CRU + FA is a viable fertilization scheme that can enhance maize growth, yield and NUE through their synergies in improving N uptake, promoting photosynthesis, increasing C/N metabolic processes, and enhancing enzyme activities. © 2021 Society of Chemical Industry.

Forest dynamics in relation to meteorology and soil in the Gulf Coast of Mexico.

Forest dynamics is complex, and the complexity could be a synthetic result of climate change. Specifically studying 11 forest type groups of the Gulf of Mexico coast region defined, we intended to explore and model the direct and indirect impacts of climate change on underlying forest dynamics. This study utilized normalized difference of vegetation index (NDVI) as a measurement indicator of forest dynamics, referring to the dynamics of canopy structure and phenology of forests, and for a given type of forests, seasonal and yearly NDVI values were applied to the quantification of its growth across the Gulf Coast. By utilizing geographically weighted regression (GWR) method, we related normalized difference vegetation index (NDVI) to precipitation, temperature, and silt and clay fractions in the soil. This study demonstrated an explanatory power of soil, besides the common macroclimate factors of precipitation, temperature, on explaining forest dynamics, which also revealed that the presence of spatiotemporal heterogeneity would affect model performance. Our results indicated that the model performance varied by forest type groups and seasons. The meteorology-soil model presented the best overall fit performance for White/Red/Jack Pine forests concerning R2 (0.952), adjusted R2 (0.905), Akaike information criterion (AIC, -1100) and residual sum of squares (RSS, 0.053) values. The comparative analysis of model performance also indicated that the meteorology-soil model has the best fit of data in summer. This study advanced the understanding of forests dynamics under conditions of climate change by highlighting the significance of soil, which is a significant confounding variable influencing forest activities but is often missed in forest-climate dynamics analysis.

[Measurement of Coronary Artery Angle in DSA Image].

OBJECTIVE: The digital subtraction angiography (DSA) image is processed to obtain central line and bifurcation point of coronary artery, and angle between blood vessels. METHODS: The image is processed on the platform of Matlab. The central line of coronary artery is extracted by Hessian matrix. The coordinates of the bifurcation point and two other points on branch vessels are obtained by central line matrix of DSA image. Then average angle of coronary artery vessels is calculated by the three points. RESULTS: For randomly selected DSA images, high accuracy values of coronary artery central line and angle may be obtained. CONCLUSIONS: Accurate measurement of coronary artery vessel angel may help operators of DSA in setting body position and help researchers in image processing.

[Development of DSA Information Management and Image Analysis System Based on Java Web].

OBJECTIVE: To establish a digital subtraction angiography (DSA) information management and image analysis system to realize scientific management of DSA image information and efficient processing of image data. METHODS: Based on Java Web under Windows 7 environment, a dynamic Browser/Server mode system was constructed by JSP and Servlet on the network. Eclipse and MySQL were used as development tool and database development platform. Tomcat network information service was used as application server. Matlab codes were embedded to analyze DSA image. RESULTS: The system consists of five modules:image information management, image processing, image analysis, advanced retrieval and clinical data management. It may complete such process as storing, deleting, saving, analyzing of DSA image and basic information of patients. CONCLUSIONS: The main interface of the system is user-friendly and easy to operate. The system will be helpful to the clinical, teaching and scientific research work related to DSA.

Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015.

Temperatures from 1982 to 2015 have exhibited an asymmetric warming pattern between day and night throughout the Yellow River Basin. The response to this asymmetric warming can be linked to vegetation growth as quantified by the NDVI (Normalized Difference Vegetation Index). In this study, the time series trends of the maximum temperature (Tmax) and the minimum temperature (Tmin) and their spatial patterns in the growing season (April-October) of the Yellow River Basin from 1982 to 2015 were analyzed. We evaluated how vegetation NDVI had responded to daytime and night-time warming, based on NDVI and meteorological parameters (precipitation and temperature) over the period 1982-2015. We found: (1) a persistent increase in the growing season Tmax and Tmin in 1982-2015 as confirmed by using the Mann-Kendall (M-K) non-parametric test method (p < 0.01), where the rate of increase of Tmin was 1.25 times that of Tmax, and thus the diurnal warming was asymmetric during 1982-2015; (2) the partial correlation between Tmax and NDVI was significantly positive only for cultivated plants, shrubs, and desert, which means daytime warming may increase arid and semi-arid vegetation's growth and coverage, and cultivated plants' growth and yield. The partial correlation between Tmin and NDVI of all vegetation types except broadleaf forest is very significant (p < 0.01) and, therefore, it has more impacts vegetation across the whole basin. This study demonstrates a methodogy for studying regional responses of vegetation to climate extremes under global climate change.

The natural history of oviposition on a ginkgophyte fruit from the Middle Jurassic of northeastern China.

A distinctive pattern of oviposition lesions occurs on a ginkgoalean seed, Yimaia capituliformis, which likely was inflicted by a kalligrammatid lacewing with a long, sword-like, plant-piercing ovipositor. This newly recorded oviposition type, DT272, occurs in the 165 million-year-old Jiulongshan Formation, of Middle Jurassic age, in Northeastern China. DT272 consists from three to seven, approximately equally spaced lesions with surrounding callus tissue, the fabricator of which targeted fleshy outer and inner tissues of a ginkgophyte fruit. This distinctive damage also is known from the fleshy attachment pad surfaces of basal bennettitalean bracts. Examination of the life history of this probable ginkgoalean-kalligrammatid oviposition interaction indicates that the spacing of the eggs in substrate tissues disfavored inter-larval contact, but little can be said of defense and counterdefense strategies between the plant host and the newly hatched immatures.

Comparison of multiple biomarkers for mortality prediction in patients with acute heart failure of ischemic and nonischemic etiology.

AIM: To investigate the prognosis of soluble ST2 (sST2), galectin-3 and N-terminal pro-B-type natriuretic peptide (NT-proBNP) for death related to ischemic and nonischemic etiology of acute heart failure (HF). METHODS: The associations between biomarkers and death were determined in 1020 patients admitted to hospital with acute HF. RESULTS: During 1-year follow-up, 162 patients died. Multivariable regression analysis showed that the hazard ratios of sST2 and NT-proBNP for 1-year all-cause death was similar and remained significant between ischemic and nonischemic HF patients. However, galectin-3 was not significantly associated with death when sST2 and NT-proBNP were incorporated into model in ischemic HF patients. CONCLUSION: There is no etiology dependent prognostic ability of NT-proBNP or ST2 in patients with acute HF, but for galectin-3 there is no added prognostic ability in ischemic HF.

Acute stimulatory effect of tumor necrosis factor on the basolateral 50 pS K channels in the thick ascending limb of the rat kidney.

The aim of the present study was to investigate the acute effect and mechanism of tumor necrosis factor (TNF) on basolateral 50 pS K channels in the thick ascending limb (TAL) of the rat kidney. The TAL tubules were isolated from the rat kidney, and the activity of the 50 pS K channels was recorded using the patch­clamp technique. The results indicated that the application of TNF (10 nM) significantly activated the 50 pS K channels and the TNF effect was concentration­dependent. Inhibition of protein kinase A, phospholipase A2 and protein tyrosine kinase using pathway inhibitors (H89, AACOCF3 and Herbimycin A, respectively) did not abolish the stimulatory effect of TNF, indicating that none of these pathways mediated the TNF effect. By contrast, the phenylarsine oxide inhibitor against protein tyrosine phosphatase (PTP) decreased the activity of the 50 pS K channels and blocked the stimulatory effect of TNF on these channels. Furthermore, western blot analysis demonstrated that the application of TNF (10 nM) in the TAL increased the phosphorylation of PTP, an indication of PTP activity stimulation. Thus, it was concluded that the acute application of TNF may stimulate the basolateral 50 pS K channel in the TAL and the stimulatory effect of TNF may be mediated by the PTP­dependent pathway.

Rethink potential risks of toxic emissions from natural gas and oil mining.

Studies have showed the increasing environmental and public health risks of toxic emissions from natural gas and oil mining, which have become even worse as fracking is becoming a dominant approach in current natural gas extraction. However, governments and communities often overlook the serious air pollutants from oil and gas mining, which are often quantified lower than the significant levels of adverse health effects. Therefore, we are facing a challenging dilemma: how could we clearly understand the potential risks of air toxics from natural gas and oil mining. This short study aims at the design and application of simple and robust methods to enhance and improve current understanding of the becoming worse toxic air emissions from natural gas and oil mining as fracking is becoming the major approach. Two simple ratios, the min-to-national-average and the max-to-national-average, are designed and applied to each type of air pollutants in a natural gas and oil mining region. The two ratios directly indicate how significantly high a type of air pollutant could be due to natural gas and oil mining by comparing it to the national average records, although it may not reach the significant risks of adverse health effects according to current risk screening methods. The min-to-national-average and the max-to-national-average ratios can be used as a direct and powerful method to describe the significance of air pollution by comparing it to the national average. The two ratios are easy to use for governments, stakeholders, and the public to pay enough attention on the air pollutants from natural gas and oil mining. The two ratios can also be thematically mapped at sampled sites for spatial monitoring, but spatial mitigation and analysis of environmental and health risks need other measurements of environmental and demographic characteristics across a natural gas and oil mining area.

Forest dynamics to precipitation and temperature in the Gulf of Mexico coastal region.

The forest is one of the most significant components of the Gulf of Mexico (GOM) coast. It provides livelihood to inhabitant and is known to be sensitive to climatic fluctuations. This study focuses on examining the impacts of temperature and precipitation variations on coastal forest. Two different regression methods, ordinary least squares (OLS) and geographically weighted regression (GWR), were employed to reveal the relationship between meteorological variables and forest dynamics. OLS regression analysis shows that changes in precipitation and temperature, over a span of 12 months, are responsible for 56% of NDVI variation. The forest, which is not particularly affected by the average monthly precipitation in most months, is observed to be affected by cumulative seasonal and annual precipitation explicitly. Temperature and precipitation almost equally impact on NDVI changes; about 50% of the NDVI variations is explained in OLS modeling, and about 74% of the NDVI variations is explained in GWR modeling. GWR analysis indicated that both precipitation and temperature characterize the spatial heterogeneity patterns of forest dynamics.

The impacts of fracking on the environment: A total environmental study paradigm.

Fracking has become a hot topic in the media and public discourse not only because of its economic benefit but also its environmental impacts. Recently, scientists have investigated the environmental impacts of fracking, and most studies focus on its air and ground water pollution. A systematic research structure and an overall evaluation of fracking's impacts on the environment are needed, because fracking does not only influence ground water but most environmental elements including but not limited to air, water, soil, rock, vegetation, wildlife, human, and many other ecosystem components. From the standpoint of the total environment, this communication assesses the overall impacts of fracking on the environment and then designs a total environmental study paradigm that effectively examines the complicated relationship among the total environment. Fracking dramatically changes the anthroposphere, which in turn significantly impacts the atmosphere, hydrosphere, lithosphere, and biosphere through the significant input or output of water, air, liquid or solid waste disposals, and the complex chemical components in fracking fluids. The proposed total environment study paradigm of fracking can be applied to other significant human activities that have dramatic impacts on the environment, such as mountain top coal mining or oil sands for environmental studies.

The spatiotemporal characteristics of environmental hazards caused by offshore oil and gas operations in the Gulf of Mexico.

Marine ecosystems are home to a host of numerous species ranging from tiny planktonic organisms, fishes, and birds, to large mammals such as the whales, manatees, and seals. However, human activities such as offshore oil and gas operations increasingly threaten marine and coastal ecosystems, for which there has been little exploration into the spatial and temporal risks of offshore oil operations. Using the Gulf of Mexico, one of the world's hottest spots of offshore oil and gas mining, as the study area, we propose a spatiotemporal approach that integrates spatial statistics and geostatistics in a geographic information system environment to provide insight to environmental management and decision making for oil and gas operators, coastal communities, local governments, and the federal government. We use the records from 1995 to 2015 of twelve types of hazards caused by offshore oil and gas operations, and analyze them spatially over a five year period. The spatial clusters of these hazards are analyzed and mapped using Getis-Ord Gi and local Moran's I statistics. We then design a spatial correlation coefficient matrix for multivariate spatial correlation, which is the ratio of the cross variogram of two types of hazards to the product of the variograms of the two hazards, showing a primary understanding of the degrees of spatial correlation among the twelve types hazards. To the best of our knowledge, it is the first application of spatiotemporal analysis methods to environmental hazards caused by offshore oil and gas operations; the proposed methods can be applied to other regions for the management and monitoring of environmental hazards caused by offshore oil operations.