Ecosystem service tradeoff and synergistic relationship in the Yellow River Delta High-Efficiency Eco-Economic Zone.

Exploring the tradeoff and synergy relationship among ecosystem services in the Yellow River Delta High-Efficiency Eco-Economic Zone is of great practical significance for regional ecosystem service function zoning and high-quality development. Using the InVEST model, spatial auto-correlation and trade-off synergism (ESTD) model, we analyzed the spatial and temporal variations of five ecosystem services (habitat quality, carbon storage, soil conservation, water conservation, and water purification), as well as their trade-off and synergistic relationships at the township scale from 2000 to 2020. The results showed that habitat quality, carbon storage, and nitrogen and phosphorus output decreased as a whole from 2000 to 2020, and soil conservation and water purification increased. Habitat quality showed a distribution pattern of high in the north and low in the south, and carbon sto-rage, nitrogen and phosphorus output, soil conservation and water purification showed a pattern of low in the north and high in the south. During the study period, synergistic relationships among the five ecosystem services were predominant in both time cross-section and time period, but there were still differences, with synergistic relationships mainly between carbon storage and other services in time cross-section, and between habitat quality and other ser-vices in time period. Our results can provide theoretical guidance and practical reference for the enhancement of ecosystem services and the zoning of ecosystem functions, as well as basic support for the optimization of spatial patterns of national territory.

Spatial and temporal distribution characteristics of typical pollution loads based on SWAT model across Tuojiang River watershed located in Sichuan Province, Southwest of China.

Tuojiang River watershed is an economically developed and densely populated area in Sichuan Province (southwest of China), which is also an important tributary of the Yangtze River. Nitrogen (N) and phosphorus (P) are the main pollutants affecting water quality, but there is still lack of study on the spatial and temporal distribution characteristics of these two pollutants. In this study, the typical non-point source pollution loads in the Tuojiang River watershed are simulated by Soil and Water Assessment Tool (SWAT) model, and the spatial autocorrelation method is used to reveal the spatial and temporal distribution characteristics of the pollution loads from the annual average and water periods. Combined with redundancy analysis (RDA) and geographically weighted regression (GWR) analysis, the main driving factors affecting the typical non-point source pollution loads in the Tuojiang River watershed are discussed from the global and local perspectives. The results show that (1) from different water periods, the pollution loads of total nitrogen (TN) and total phosphorus (TP) in three water periods show obviously different, is the highest in the abundant water period, with 323.4 kg/ha and 47.9 kg/ha, followed by the normal water period, with 95.7 kg/ha and 14.1 kg/ha, and the lowest in the dry water period, with 28.4 kg/ha and 4.2 kg/ha. The annual average value of TN pollution load is higher than that of TP, with 447.5 kg/ha and 66.1 kg/ha, respectively; (2) the TN and TP pollution loads are stable on the whole, and the overall level in the middle reaches is higher. The pollution loads of Shifang City and Mianzhu City are higher in all three water periods. (3) Elevation and slope are two main driving factors affecting the TN and TP pollution loads in the Tuojiang River watershed. Therefore, the visualization and quantification of temporal and spatial distribution characteristics of typical non-point source pollution loads in the Tuojiang River watershed are helpful to provide the basis for scientific prevention and control of pollution in the Tuojiang River watershed and are of great significance to promote the sustainable, coordinated, and healthy development of water environment and economy in the watershed.

Effects of sampling strategy in rivers on load estimation for Nitrate-Nitrogen and total Phosphorus in a lowland agricultural area.

The transport of nutrients into water bodies is one of the main causes of water eutrophication. It is therefore important to estimate the loads of nutrients. Discharge and nutrient concentrations are the fundamental elements to estimate the loads of nutrients, the latter can be affected by sampling strategies. As conducting sampling campaign and laboratory analysis are both expensive, it is necessary to find the best effective sampling strategy. The aim of this paper is to show how autocorrelation and standard statistical methods can be used to test the effects of different sampling strategies on the nutrient load estimation and to find the optimal sampling strategy. The data set in this study is from the 50 km² Kielstau catchment, a UNESCO demo site for ecohydrology in Northern Germany and consists of 14 years daily values of climate, hydrology, and water quality from 2006 to 2019. We calculated the autocorrelation (AC) of discharge (Q), precipitation, Nitrate-Nitrogen (NO3-N) and total Phosphorus (Ptot). Then we tested the effects of sampling intervals from 7 to 56 days (1-8 weeks) on the nutrient loads. Our results showed a high AC of Q and NO3-N for a long period, but the AC of Ptot and precipitation decreased very fast. An increase of the sampling interval (less frequent) increased the error of estimating the concentrations and loads. Consequently, we recommend that (1) the optimal sampling strategy for nutrient load estimation in an agriculture-dominant catchment should be continuously monitoring discharge combined with periodic grabbed samples; (2) the sampling frequency for NO3-N is suggested to be monthly (every 28 days) and for Ptot weekly (every 7 days). The information will help those tasked with catchment monitoring to design appropriate sampling strategy to ensure adequate data for nutrients load estimation in lowland rivers.

Space-time dynamics regression models to assess variations of composite index for anthropometric failure across the administrative zones in Ethiopia.

BACKGROUND: A single anthropometric index such as stunting, wasting, or underweight does not show the holistic picture of under-five children's undernutrition status. To alleviate this problem, we adopted a multifaceted single index known as the composite index for anthropometric failure (CIAF). Using this undernutrition index, we investigated the disparities of Ethiopian under-five children's undernutrition status in space and time. METHODS: Data for analysis were extracted from the Ethiopian Demographic and Health Surveys (EDHSs). The space-time dynamics models were formulated to explore the effects of different covariates on undernutrition among children under five in 72 administrative zones in Ethiopia. RESULTS: The general nested spatial-temporal dynamic model with spatial and temporal lags autoregressive components was found to be the most adequate (AIC = -409.33, R2 = 96.01) model. According to the model results, the increase in the percentage of breastfeeding mothers in the zone decreases the CIAF rates of children in the zone. Similarly, the increase in the percentages of parental education, and mothers' nutritional status in the zones decreases the CIAF rate in the zone. On the hand, increased percentages of households with unimproved water access, unimproved sanitation facilities, deprivation of women's autonomy, unemployment of women, and lower wealth index contributed to the increased CIAF rate in the zone. CONCLUSION: The CIAF risk factors are spatially and temporally correlated across 72 administrative zones in Ethiopia. There exist geographical differences in CIAF among the zones, which are influenced by spatial neighborhoods of the zone and temporal lags within the zone. Hence these findings emphasize the need to take the spatial neighborhood and historical/temporal contexts into account when planning CIAF prevention.

[Quantitative analysis of total content differentiation and driving factors of phenolic acids in Angelicae Sinensis Radix of Gansu Dao-di herbs from perspective of geographical space].

Dao-di herbs, produced in a specific region and screened through long-term clinical application, is characterized by high stable quality, good efficacy, and high popularity. With favorable climate conditions, Gansu gives birth to the Dao-di herbs Angelicae Sinensis Radix which is widely used in clinical practice, and multiple regions in Gansu, with similar ecological environment produce Angelicae Sinensis Radix. In this study, the spatial correlation and difference of phenolic acid content in Angelicae Sinensis Radix from Dao-di producing areas, emerging producing areas, and emerging planting areas in Gansu were analyzed based on ArcGIS to explore the "quality(chemical type)" characteristics of genuine Angelicae Sinensis Radix. Moreover, spatial distribution law and main driving factors of the total phenolic acid content in Angelicae Sinensis Radix in Gansu were analyzed based on geodetecctor. This study is expected to lay a basis for Dao-di research and production regionalization of Angelicae Sinensis Radix.

Emergent spatial patterns of competing benthic and pelagic algae in a river network: A parsimonious basin-scale modeling analysis.

Algae, as primary producers in riverine ecosystems, are found in two distinct habitats: benthic and pelagic algae typically prevalent in shallow/small and deep/large streams, respectively. Over an entire river continuum, spatiotemporal patterns of the two algal communities reflect specificity in habitat preference determined by geomorphic structure, hydroclimatic controls, and spatiotemporal heterogeneity in nutrient loads from point- and diffuse-sources. By representing these complex interactions between geomorphic, hydrologic, geochemical, and ecological processes, we present here a new river-network-scale dynamic model (CnANDY) for pelagic (A) and benthic (B) algae competing for energy and one limiting nutrient (phosphorus, P). We used the urbanized Weser River Basin in Germany (7th-order; ~8.4 million population; ~46 K km2) as a case study and analyzed simulations for equilibrium mass and concentrations under steady median river discharge. We also examined P, A, and B spatial patterns in four sub-basins. We found an emerging pattern characterized by scaling of P and A concentrations over stream-order ω, whereas B concentration was described by three distinct phases. Furthermore, an abrupt algal regime shift occurred in intermediate streams from B dominance in ω≤3 to exclusive A presence in ω≥6. Modeled and long-term basin-scale monitored dissolved P concentrations matched well for ω>4, and with overlapping ranges in ω<3. Power-spectral analyses for the equilibrium P, A, and B mass distributions along hydrological flow paths showed stronger clustering compared to geomorphological attributes, and longer spatial autocorrelation distance for A compared to B. We discuss the implications of our findings for advancing hydro-ecological concepts, guiding monitoring, informing management of water quality, restoring aquatic habitat, and extending CnANDY model to other river basins.

Spatial distribution and determinants of health loss from Kashin-Beck disease in Bin County, Shaanxi Province, China.

BACKGROUND: Kashin-Beck disease (KBD) is one of the major endemic diseases in China, which severely impacts the physical health and life quality of people. A better understanding of the spatial distribution of the health loss from KBD and its influencing factors will help to identify areas and populations at high risk so as to plan for targeted interventions. METHODS: The data of patients with KBD at village-level were collected to estimate and analyze the spatial pattern of health loss from KBD in Bin County, Shaanxi Province. The years lived with disability (YLDs) index was applied as a measure of health loss from KBD. Spatial autocorrelation methodologies, including Global Moran's I and Local Moran's I, were used to describe and map spatial clusters of the health loss. In addition, basic individual information and environmental samples were collected to explore natural and social determinants of the health loss from KBD. RESULTS: The estimation of YLDs showed that patients with KBD of grade II and patients over 50 years old contributed most to the health loss of KBD in Bin County. No significant difference was observed between two genders. The spatial patterns of YLDs and YLD rate of KBD were clustered significantly at both global and local scales. Villages in the southwestern and eastern regions revealed higher health loss, while those in the northern regions exhibited lower health loss. This clustering was found to be significantly related to organically bound Se in soil and poverty rate of KBD patients. CONCLUSIONS: Our results suggest that future treatment and prevention of KBD should focus on endemic areas with high organically bound Se in soil and poor economic conditions. The findings can also provide important information for further exploration of the etiology of KBD.

Quantitative analysis of total content differentiation and driving factors of phenolic acids in Angelicae Sinensis Radix of Gansu Dao-di herbs from perspective of geographical space / 中国中药杂志

Dao-di herbs, produced in a specific region and screened through long-term clinical application, is characterized by high stable quality, good efficacy, and high popularity. With favorable climate conditions, Gansu gives birth to the Dao-di herbs Angelicae Sinensis Radix which is widely used in clinical practice, and multiple regions in Gansu, with similar ecological environment produce Angelicae Sinensis Radix. In this study, the spatial correlation and difference of phenolic acid content in Angelicae Sinensis Radix from Dao-di producing areas, emerging producing areas, and emerging planting areas in Gansu were analyzed based on ArcGIS to explore the "quality(chemical type)" characteristics of genuine Angelicae Sinensis Radix. Moreover, spatial distribution law and main driving factors of the total phenolic acid content in Angelicae Sinensis Radix in Gansu were analyzed based on geodetecctor. This study is expected to lay a basis for Dao-di research and production regionalization of Angelicae Sinensis Radix.

The self in art therapy - Brain-based assessment of the drawing process.

Art therapy plays important role in classical psychological assessment as it allows expressing the subject's sense of self. However, its effectiveness and validity could be impeded by lack of relationship to the patients' neuronal changes in their brain. The aim of our theoretical-empirical paper is to propose a novel brain-based quantitative objective measurement of the self and how it shapes the drawing process. We discuss recent data that how the autocorrelation window (ACW) is related to the temporal continuity of self in current neuroscience and further develop a method to use ACW to measure the temporal continuity of the drawing process, probing it in two case studies. As expected, the schizophrenic subject shows lower ACW values compared to the healthy subject and reflects the well-known deficit in the temporal continuity of the self in schizophrenia. We concluded that ACW and eventually other measures of the brain's spatiotemporal structure might be able to serve as objective markers of the self in the drawing process. As our approach connects brain, self, and drawing process, it provides the theoretical basis for the future development of a brain-based assessment of the self in the drawing process and art therapy.

Diatoms as indicators of the multivariate environment of mountain lakes.

Diatom assemblages are used widely as indicators of environmental conditions. They have been particularly useful in the assessment of some acute environmental problems such as water acidification, eutrophication and salinisation. High mountain lakes are currently a research focus as sentinels of global change, predominantly of long-distance atmospheric pollution and climate warming. The diatom assemblages in these lakes are extremely rich, and the sediment record provides short and long historical perspectives of the changes. We investigated the relative sensitivity of diatoms to the main environmental gradients found in mountain lakes and evaluated the strength and uncertainties for predicting simultaneously several environmental variables associated with these gradients based on a survey of 83 lakes in the Pyrenees. Variables related to the ionic composition and acid-base balance (calcium*, acid neutralizing capacity*, magnesium, sodium, and sulphates), trophic conditions (total phosphorus* and dissolved organic carbon) and physical factors (water temperature*, irradiance at the lake bottom*, and macrophyte cover) explained independently the variation in the diatom assemblages. However, the assemblage predictive capacity of these variables- tested by developing transfer functions (Weighted Averaging- Partial Least Squares) - was only acceptable for a subset of the variables (*). The spatial autocorrelation of the environmental variables had no influence on the performance of the transfer functions except for water temperature, which is highly dependent on altitude. Our results indicate that diatom assemblages have great potential for assessments of multiple environmental variables in mountain lakes and, consequently, in applications of global change surveillance.

A Spatiotemporal Analysis and Dispersal Patterns of the Potato Cyst Nematode Globodera pallida in Idaho.

The potato cyst nematode Globodera pallida is a globally regulated potato pest. It was detected for the first time in the United States in the state of Idaho in 2006, and as of February 2019, the infestation is limited to 1,326 hectares. G. pallida is a specialized obligate sedentary endoparasite that can survive in the soil for up to 30 years in the absence of its potato host. In highly infested fields, the nematode can reduce tuber yields up to 80% and is spread mainly through the movement of soil, tubers, or farm equipment. The objectives of this study were to describe the spatiotemporal pattern of G. pallida in infested fields and model its dispersal patterns in southeastern Idaho. We used geostatistical tools and simulation models for precise mapping and to describe the relationships between G. pallida incidence and the spatial configurations. We found that the nematode is spatially clustered and prevalent around edges of fields, and its dispersal pattern followed the direction of cultivation. We found that the absence of potato in an infested field significantly reduced the number of cysts sampled each year subsequent to the initial delimitation sampling in 2007. Phytosanitary measures prohibiting the growth of potato contributed to stopping nematode reproduction, and the use of chemical fumigants and biofumigant cover crops contributed to a significant reduction in egg viability. We observed a process of a nonlinear decline in the prevalence of cysts as the distance separation from the primary infestation focus increased. A power law model was used to fit G. pallida dispersal capabilities. This study contributed to describing G. pallida infestation for Idaho. The goal of this study is to provide information on the spatial pattern and landscape ecology of G. pallida in Idaho for policy makers, industry, and researchers as well as facilitate common understandings on the challenges and opportunities for controlling this pest in Idaho.

Assisted baseline subtraction in complex chromatograms using the BEADS algorithm.

The data processing step of complex signals in high-performance liquid chromatography may constitute a bottleneck to obtain significant information from chromatograms. Data pre-processing should be preferably done with little (or no) user supervision, for a maximal benefit and highest speed. In this work, a tool for the configuration of a state-of-the-art baseline subtraction algorithm, called BEADS (Baseline Estimation And Denoising using Sparsity) is developed and verified. A quality criterion based on the measurement of the autocorrelation level was designed to select the most suitable working parameters to obtain the best baseline. The use of a log transformation of the signal attenuated artifacts associated to a large disparity in signal size between sample constituents. Conventional BEADS makes use of trial-and-error strategies to set up the working parameters, which makes the process slow and inconsistent. This constitutes a major drawback in its successful application. In contrast, the assisted BEADS simplifies the setup, shortens the processing time and makes the baseline subtraction more reliable. The assisted algorithm was tested on several complex chromatograms corresponding to extracts of medicinal herbs analysed with acetonitrile-water gradients, and a mixture of sulphonamides eluted with acetonitrile gradients in the presence of the non-ionic surfactant Brij-35 under micellar conditions.

Detecting time-specific differences between temporal nonlinear curves: Analyzing data from the visual world paradigm.

In multiple fields of study, time series measured at high frequencies are used to estimate population curves that describe the temporal evolution of some characteristic of interest. These curves are typically nonlinear, and the deviations of each series from the corresponding curve are highly autocorrelated. In this scenario, we propose a procedure to compare the response curves for different groups at specific points in time. The method involves fitting the curves, performing potentially hundreds of serially correlated tests, and appropriately adjusting the overall alpha level of the tests. Our motivating application comes from psycholinguistics and the visual world paradigm. We describe how the proposed technique can be adapted to compare fixation curves within subjects as well as between groups. Our results lead to conclusions beyond the scope of previous analyses.

Assessment of drug-induced arrhythmic risk using limit cycle and autocorrelation analysis of human iPSC-cardiomyocyte contractility.

Cardiac safety assays incorporating label-free detection of human stem-cell derived cardiomyocyte contractility provide human relevance and medium throughput screening to assess compound-induced cardiotoxicity. In an effort to provide quantitative analysis of the large kinetic datasets resulting from these real-time studies, we applied bioinformatic approaches based on nonlinear dynamical system analysis, including limit cycle analysis and autocorrelation function, to systematically assess beat irregularity. The algorithms were integrated into a software program to seamlessly generate results for 96-well impedance-based data. Our approach was validated by analyzing dose- and time-dependent changes in beat patterns induced by known proarrhythmic compounds and screening a cardiotoxicity library to rank order compounds based on their proarrhythmic potential. We demonstrate a strong correlation for dose-dependent beat irregularity monitored by electrical impedance and quantified by autocorrelation analysis to traditional manual patch clamp potency values for hERG blockers. In addition, our platform identifies non-hERG blockers known to cause clinical arrhythmia. Our method provides a novel suite of medium-throughput quantitative tools for assessing compound effects on cardiac contractility and predicting compounds with potential proarrhythmia and may be applied to in vitro paradigms for pre-clinical cardiac safety evaluation.