Spatial organization of lysosomal exocytosis relies on membrane tension gradients.

Lysosomal exocytosis is involved in many key cellular processes but its spatiotemporal regulation is poorly known. Using total internal reflection fluorescence microscopy (TIRFM) and spatial statistics, we observed that lysosomal exocytosis is not random at the adhesive part of the plasma membrane of RPE1 cells but clustered at different scales. Although the rate of exocytosis is regulated by the actin cytoskeleton, neither interfering with actin or microtubule dynamics by drug treatments alters its spatial organization. Exocytosis events partially co-appear at focal adhesions (FAs) and their clustering is reduced upon removal of FAs. Changes in membrane tension following a hypo-osmotic shock or treatment with methyl-ß-cyclodextrin were found to increase clustering. To investigate the link between FAs and membrane tension, cells were cultured on adhesive ring-shaped micropatterns, which allow to control the spatial organization of FAs. By using a combination of TIRFM and fluorescence lifetime imaging microscopy (FLIM), we revealed the existence of a radial gradient in membrane tension. By changing the diameter of micropatterned substrates, we further showed that this gradient as well as the extent of exocytosis clustering can be controlled. Together, our data indicate that the spatial clustering of lysosomal exocytosis relies on membrane tension patterning controlled by the spatial organization of FAs.

A unified framework for species spatial patterns: Linking the occupancy area curve, Taylor's Law, the neighborhood density function and two-plot species turnover.

The description of spatial patterns in species distributions is central to research throughout ecology. In this manuscript, we demonstrate that five of the most widely used species-level spatial patterns are not only related, but can in fact be quantitatively derived from each other under minimal assumptions: the occupancy area curve, Taylor's Law, the neighborhood density function, a two-plot variant of Taylor's Law and two-plot single-species turnover. We present an overarching mathematical framework and derivations for several theoretical example cases, along with a simulation study and empirical analysis that applies the framework to data from the Barro Colorado Island tropical forest plot. We discuss how knowledge of this mathematical relationship can support the testing of ecological theory, suggest efficient field sampling schemes, highlight the relative importance of plot area and abundance in driving turnover patterns and lay the groundwork for future unified theories of community-level spatial metrics and multi-patch spatial patterns.

Spatial distribution of six managed tree species is influenced by topography conditions in the Central Amazon.

In tropical forests, the spatial distribution of trees may present random, uniform, or grouped patterns that can simultaneously be affected by site and species characteristics. In Central Amazon, topographic gradients and soil water levels drive differences in tree species distribution and in forest dynamics at local scales. Knowing this kind of information can be useful for a forest manager to plan harvesting operations considering the microhabitat preference of merchantable species to reduce the disturbances caused by logging activities. Thus, the spatial variation of tree species is an important information to be considered to support the planning process of forest logging. The present study aims to evaluate the spatial distribution pattern of six species and analyze the relationship between the topography and the population densities and stem size of those species. The study was carried out in a forest production compartment managed by a private company located in the municipality of Silves, state of Amazonas, Brazil. The spatial pattern of the six species was characterized by Ripley's K function. Spatial distribution of diameter at breast height (DBH) and tree density based on kernel incidence calculation were evaluated for topographic classes of slope, elevation, and distance from streams, which were mapped using geographic information systems (GIS). The means of DBH and density of each species were compared among topographic classes by ANOVA and Tukey's test. The results demonstrated the predominance of the aggregate distribution pattern for the six species up to 1105 m (p < 0.01). The tree species Minquartia guianensis Aubl., Protium puncticulatum J.F.Macbr, Manilkara elata (Allemão ex Miq.) Monach, and Caryocar glabrum Aubl. Pers showed an increase in the tree density as the distance from the streams and elevation increased, standing spatially incident on plateaus. Kernel densities of Dinizia excelsa Ducke and Goupia glabra Aubl. were higher closer to streams. The DBH averages followed similar trends of population density for M. guianensis, M. elata, and C. glabrum, and the opposite pattern for D. excelsa, which presented larger individuals in less densely populated areas. P. puncticulatum and G. glabra mean DBH distribution was not affected by the topographic variables analyzed. Topography-related variables showed effects on variations of density and tree size, suggesting that species may be spatially sensitive to the habitat variability available in the study area. In view of logging planning, spatial distribution must be considered in decisions related to cutting down trees and maintenance of remaining trees, especially because some species are more aggregated in smaller scales. Moreover, as topographic variations affect the spatial distribution of size and density, the timber yield will vary spatially in the area, bringing implications for planning logging intensities, roads, skid trails and forest operations. Finally, the procedures and information generated in this study can be reproduced and applied to other species and managed areas to support the planning toward minimizing impacts on the spatial structure of commercial species, as well as to increase the chances of future stock recovery of managed forests in the Amazon.

Spatial and Temporal Dynamics of Mal Secco Disease Spread in Lemon Orchards in Israel.

Mal Secco is a severe disease of citrus in which the fungus Plenodomus tracheiphilus (formerly Phoma tracheiphila) penetrates the vascular system of the host. In this study, we characterized the spatial dynamics of the disease in seven lemon orchards. A representative block of trees from each orchard was evaluated monthly during 3 consecutive years. In addition, scouts assessed disease severity in 75 orchards from three different geographical regions and tested for association between disease severity and measures of orchard management, environmental factors, cultural practices, and cultivar type. We assessed disease incidence and characteristics of spatial patterns using Ripley's K function and fitted logistic regression models for different neighboring tree structures followed by model selection methods to provide insight into the spatial and temporal dynamics of disease progress. We found different rates of disease spread in different orchards, which are most likely the result of differences in orchard management practices or less likely the result of differences in climatic conditions. There was an indication that agricultural tools contribute to spread of the disease within rows of trees. The results confirm that the lemon cultivar Interdonato is less susceptible compared with other citrus cultivars, and they suggest that the density of urban terrain surrounding each orchard is positively correlated with the severity of the disease. In contrast to our expectations, no correlation was found between the density of lemon orchards surrounding an orchard and the severity of the disease within it, which corroborates previous findings regarding the limited distribution of the disease.

Spatio-temporal analysis of oak decline process in open woodlands: A case study in SW Spain.

This study aims to characterize at landscape level the spatio-temporal dynamics of a massive oak decline that is occurring in dehesas ecosystems. We are looking at possibilities of matching with Phytophthora disease behavior, a harmful disease detected in the studied area, in order to interpret its implications within the context of the disease management. Spatial locations of affected trees from 2001, 2009 and 2016 identified through photointerpretation were analyzed with the inhomogeneous Ripley's K-function to assess their spatial pattern. Multivariate Adaptive Regression Splines (MARS), a non-parametric data mining method, was used to investigate the influence of a range of landscape descriptors of different nature on the proneness to oak decline, using the location of affected trees in comparison with that of healthy spots (points randomly extracted from areas covered by healthy trees). Affected trees showed a strong clustering pattern that decreased over time. The reported spatial patterns align with the hypothesis of Phytophthora cinnamomi Rands. being the main cause of oak decline in Mediterranean forests. Location of affected trees detected in different years was found to be spatially related, suggesting the implication of a contagion process. MARS models from 2001, 2009 and 2016 reported Area Under the Curve (AUC) values of 0.707, 0.671 and 0.651, respectively. Slope was the most influential landscape descriptor across the three years, with distance to afforestations being the second for 2001 and 2009. Landscape descriptors linked to human factors and soil water content seem to influence oak decline caused by Phytophthora cinnamomi at landscape level. Afforestations carried out as part of the afforestation subsidy program promoted by the European Commission in 1992 could have acted as an initial source of Phytophthora cinnamomi infection. These findings together with the consideration of the spatial and temporal scale of the spreading are essential when planning the management of oak decline in open woodlands.

3D super-resolution microscopy reflects mitochondrial cristae alternations and mtDNA nucleoid size and distribution.

3D super-resolution microscopy based on the direct stochastic optical reconstruction microscopy (dSTORM) with primary Alexa-Fluor-647-conjugated antibodies is a powerful method for accessing changes of objects that could be normally resolved only by electron microscopy. Despite the fact that mitochondrial cristae yet to become resolved, we have indicated changes in cristae width and/or morphology by dSTORM of ATP-synthase F1 subunit α (F1α). Obtained 3D images were analyzed with the help of Ripley's K-function modeling spatial patterns or transferring them into distance distribution function. Resulting histograms of distances frequency distribution provide most frequent distances (MFD) between the localized single antibody molecules. In fasting state of model pancreatic ß-cells, INS-1E, MFD between F1α were ~80 nm at 0 and 3 mM glucose, whereas decreased to 61 nm and 57 nm upon glucose-stimulated insulin secretion (GSIS) at 11 mM and 20 mM glucose, respectively. Shorter F1α interdistances reflected cristae width decrease upon GSIS, since such repositioning of F1α correlated to average 20 nm and 15 nm cristae width at 0 and 3 mM glucose, and 9 nm or 8 nm after higher glucose simulating GSIS (11, 20 mM glucose, respectively). Also, submitochondrial entities such as nucleoids of mtDNA were resolved e.g. after bromo-deoxyuridine (BrDU) pretreatment using anti-BrDU dSTORM. MFD in distances distribution histograms reflected an average nucleoid diameter (<100 nm) and average distances between nucleoids (~1000 nm). Double channel PALM/dSTORM with Eos-lactamase-ß plus anti-TFAM dSTORM confirmed the latter average inter-nucleoid distance. In conclusion, 3D single molecule (dSTORM) microscopy is a reasonable tool for studying mitochondrion.

Quantitative spatial analysis of transcripts in multinucleate cells using single-molecule FISH.

mRNA positioning in the cell is important for diverse cellular functions and proper development of multicellular organisms. Single-molecule RNA FISH (smFISH) enables quantitative investigation of mRNA localization and abundance at the level of individual molecules in the context of cellular features. Details about spatial mRNA patterning at various times, in different genetic backgrounds, at different developmental stages, and under varied environmental conditions provide invaluable insights into the mechanisms and functions of spatial regulation. Here, we describe detailed methods for performing smFISH along with immunofluorescence for two large, multinucleate cell types: the fungus Ashbya gossypii and cultured mouse myotubes. We also put forward a semi-automated image processing tool that systematically detects mRNAs from smFISH data and statistically analyzes the spatial pattern of mRNAs using a customized MATLAB code. These protocols and image analysis tools can be adapted to a wide variety of transcripts and cell types for systematically and quantitatively analyzing mRNA distribution in three-dimensional space.

Managing distance and covariate information with point-based clustering.

BACKGROUND: Geographic perspectives of disease and the human condition often involve point-based observations and questions of clustering or dispersion within a spatial context. These problems involve a finite set of point observations and are constrained by a larger, but finite, set of locations where the observations could occur. Developing a rigorous method for pattern analysis in this context requires handling spatial covariates, a method for constrained finite spatial clustering, and addressing bias in geographic distance measures. An approach, based on Ripley's K and applied to the problem of clustering with deliberate self-harm (DSH), is presented. METHODS: Point-based Monte-Carlo simulation of Ripley's K, accounting for socio-economic deprivation and sources of distance measurement bias, was developed to estimate clustering of DSH at a range of spatial scales. A rotated Minkowski L1 distance metric allowed variation in physical distance and clustering to be assessed. Self-harm data was derived from an audit of 2 years' emergency hospital presentations (n = 136) in a New Zealand town (population ~50,000). Study area was defined by residential (housing) land parcels representing a finite set of possible point addresses. RESULTS: Area-based deprivation was spatially correlated. Accounting for deprivation and distance bias showed evidence for clustering of DSH for spatial scales up to 500 m with a one-sided 95 % CI, suggesting that social contagion may be present for this urban cohort. CONCLUSIONS: Many problems involve finite locations in geographic space that require estimates of distance-based clustering at many scales. A Monte-Carlo approach to Ripley's K, incorporating covariates and models for distance bias, are crucial when assessing health-related clustering. The case study showed that social network structure defined at the neighbourhood level may account for aspects of neighbourhood clustering of DSH. Accounting for covariate measures that exhibit spatial clustering, such as deprivation, are crucial when assessing point-based clustering.

Maintaining distances with the engineer: patterns of coexistence in plant communities beyond the patch-bare dichotomy.

Two-phase plant communities with an engineer conforming conspicuous patches and affecting the performance and patterns of coexisting species are the norm under stressful conditions. To unveil the mechanisms governing coexistence in these communities at multiple spatial scales, we have developed a new point-raster approach of spatial pattern analysis, which was applied to a Mediterranean high mountain grassland to show how Festuca curvifolia patches affect the local distribution of coexisting species. We recorded 22 111 individuals of 17 plant perennial species. Most coexisting species were negatively associated with F. curvifolia clumps. Nevertheless, bivariate nearest-neighbor analyses revealed that the majority of coexisting species were confined at relatively short distances from F. curvifolia borders (between 0-2 cm and up to 8 cm in some cases). Our study suggests the existence of a fine-scale effect of F. curvifolia for most species promoting coexistence through a mechanism we call 'facilitation in the halo'. Most coexisting species are displaced to an interphase area between patches, where two opposite forces reach equilibrium: attenuated severe conditions by proximity to the F. curvifolia canopy (nutrient-rich islands) and competitive exclusion mitigated by avoiding direct contact with F. curvifolia.

A Hypothesis Test for Detecting Distance-Specific Clustering and Dispersion in Areal Data.

Spatial clustering detection has a variety of applications in diverse fields, including identifying infectious disease outbreaks, pinpointing crime hotspots, and identifying clusters of neurons in brain imaging applications. Ripley's K-function is a popular method for detecting clustering (or dispersion) in point process data at specific distances. Ripley's K-function measures the expected number of points within a given distance of any observed point. Clustering can be assessed by comparing the observed value of Ripley's K-function to the expected value under complete spatial randomness. While performing spatial clustering analysis on point process data is common, applications to areal data commonly arise and need to be accurately assessed. Inspired by Ripley's K-function, we develop the positive area proportion function (PAPF) and use it to develop a hypothesis testing procedure for the detection of spatial clustering and dispersion at specific distances in areal data. We compare the performance of the proposed PAPF hypothesis test to that of the global Moran's I statistic, the Getis-Ord general G statistic, and the spatial scan statistic with extensive simulation studies. We then evaluate the real-world performance of our method by using it to detect spatial clustering in land parcels containing conservation easements and US counties with high pediatric overweight/obesity rates.

Nano-Architecture of Persistent Focal DNA Damage Regions in the Minipig Epidermis Weeks after Acute γ-Irradiation.

Exposure to high acute doses of ionizing radiation (IR) can induce cutaneous radiation syndrome. Weeks after such radiation insults, keratinocyte nuclei of the epidermis exhibit persisting genomic lesions that present as focal accumulations of DNA double-strand break (DSB) damage marker proteins. Knowledge about the nanostructure of these genomic lesions is scarce. Here, we compared the chromatin nano-architecture with respect to DNA damage response (DDR) factors in persistent genomic DNA damage regions and healthy chromatin in epidermis sections of two minipigs 28 days after lumbar irradiation with ~50 Gy γ-rays, using single-molecule localization microscopy (SMLM) combined with geometric and topological mathematical analyses. SMLM analysis of fluorochrome-stained paraffin sections revealed, within keratinocyte nuclei with perisitent DNA damage, the nano-arrangements of pATM, 53BP1 and Mre11 DDR proteins in γ-H2AX-positive focal chromatin areas (termed macro-foci). It was found that persistent macro-foci contained on average ~70% of 53BP1, ~23% of MRE11 and ~25% of pATM single molecule signals of a nucleus. MRE11 and pATM fluorescent tags were organized in focal nanoclusters peaking at about 40 nm diameter, while 53BP1 tags formed nanoclusters that made up super-foci of about 300 nm in size. Relative to undamaged nuclear chromatin, the enrichment of DDR protein signal tags in γ-H2AX macro-foci was on average 8.7-fold (±3) for 53BP1, 3.4-fold (±1.3) for MRE11 and 3.6-fold (±1.8) for pATM. The persistent macro-foci of minipig epidermis displayed a ~2-fold enrichment of DDR proteins, relative to DSB foci of lymphoblastoid control cells 30 min after 0.5 Gy X-ray exposure. A lasting accumulation of damage signaling and sensing molecules such as pATM and 53BP1, as well as the DSB end-processing protein MRE11 in the persistent macro-foci suggests the presence of diverse DNA damages which pose an insurmountable problem for DSB repair.

Advanced image-free analysis of the nano-organization of chromatin and other biomolecules by Single Molecule Localization Microscopy (SMLM).

The cell as a system of many components, governed by the laws of physics and chemistry drives molecular functions having an impact on the spatial organization of these systems and vice versa. Since the relationship between structure and function is an almost universal rule not only in biology, appropriate methods are required to parameterize the relationship between the structure and function of biomolecules and their networks, the mechanisms of the processes in which they are involved, and the mechanisms of regulation of these processes. Single molecule localization microscopy (SMLM), which we focus on here, offers a significant advantage for the quantitative parametrization of molecular organization: it provides matrices of coordinates of fluorescently labeled biomolecules that can be directly subjected to advanced mathematical analytical procedures without the need for laborious and sometimes misleading image processing. Here, we propose mathematical tools for comprehensive quantitative computer data analysis of SMLM point patterns that include Ripley distance frequency analysis, persistent homology analysis, persistent 'imaging', principal component analysis and co-localization analysis. The application of these methods is explained using artificial datasets simulating different, potentially possible and interpretatively important situations. Illustrative analyses of real complex biological SMLM data are presented to emphasize the applicability of the proposed algorithms. This manuscript demonstrated the extraction of features and parameters quantifying the influence of chromatin (re)organization on genome function, offering a novel approach to study chromatin architecture at the nanoscale. However, the ability to adapt the proposed algorithms to analyze essentially any molecular organizations, e.g., membrane receptors or protein trafficking in the cytosol, offers broad flexibility of use.

Case report of congenital methemoglobinemia: an uncommon cause of neonatal cyanosis.

BACKGROUND: Methemoglobinemia can be an acquired or congenital condition. The acquired form occurs from exposure to oxidative agents. Congenital methemoglobinemia is a rare and potentially life-threatening cause of cyanosis in newborns that can be caused by either cytochrome B5 reductase or hemoglobin variants known as Hemoglobin M. CASE PRESENTATION: A term male infant developed cyanosis and hypoxia shortly after birth after an uncomplicated pregnancy, with oxygen saturations persistently 70-80% despite 1.0 FiO2 and respiratory support of CPAP+ 6 cm H2O. Pre- and post-ductal saturations were equal and remained below 85%. Initial radiographic and echography imaging was normal. Capillary blood gas values were reassuring with normal pH and an elevated pO2. Investigations to rule out hemolysis and end-organ dysfunction were within acceptable range. Given the absence of clear cardiac or pulmonary etiology of persistent cyanosis, hematologic causes such as methemoglobinemia were explored. No family history was available at the time of transfer to our institution. Unconjugated hyperbilirubinemia > 5 mg/dL (442 µmol/L) interfered with laboratory equipment measurement, making accurate methemoglobin levels unattainable despite multiple attempts. Initial treatment with methylene blue or ascorbic acid was considered. However, upon arrival of the presumed biological father, a thorough history revealed an extensive paternal family history of neonatal cyanosis due to a rare mutation resulting in a hemoglobin M variant. Given this new information, hematology recommended supportive care as well as further testing to confirm the diagnosis of congenital methemoglobinopathy. Whole genome sequencing revealed a likely pathogenic variation in hemoglobin. The neonate was discharged home at 2 weeks of age on full oral feeds with 0.25 L/min nasal cannula as respiratory support, with close outpatient follow-up. By 5 weeks of age, he was weaned off respiratory support. CONCLUSION: Congenital methemoglobinemia should be considered in the differential diagnosis for newborns with persistent hypoxemia despite normal imaging and laboratory values. Accurate quantification of methemoglobin concentrations is challenging in neonates due to the presence of other substances that absorb light at similar wavelengths, including HbF, bilirubin, and lipids.

Spatial pattern analysis of line-segment data in ecology.

The spatial analysis of linear features (lines and curves) is a challenging and rarely attempted problem in ecology. Existing methods are typically expressed in abstract mathematical formalism, making it difficult to assess their relevance and transferability into an ecological setting. We introduce a set of concrete and accessible methods to analyze the spatial patterning of line-segment data. The methods include Monte Carlo techniques based on a new generalization of Ripley's K -function and a class of line-segment processes that can be used to specify parametric models: parameters are estimated using maximum likelihood and models compared using information-theoretic principles. We apply the new methods to fallen tree (dead log) data collected from two 1-ha Australian tall eucalypt forest plots. Our results show that the spatial pattern of the fallen logs is best explained by plot-level spatial heterogeneity in combination with a slope-dependent nonuniform distribution of fallen-log orientations. These methods are of a general nature and are applicable to any line-segment data. In the context of forest ecology, the integration of fallen logs as linear structural features in a landscape with the point locations of living trees, and a quantification of their interactions, can yield new insights into the functional and structural role of tree fall in forest communities and their enduring post-mortem ecological legacy as spatially distributed decomposing logs.

Super-Resolution Imaging and Spatial Analysis of RAS on Intact Plasma Membrane Sheets.

The function of lipid-anchored small GTPases RAS proteins is mostly compartmentalized to the plasma membrane (PM). Complex biophysical interactions between the C-terminal membrane-anchoring domains of RAS isoforms and PM lipids drive spatial segregation of RAS molecules in the formation of nanometer-sized domains, termed as nanoclusters. These RAS/lipid proteolipid nano-assemblies are the main sites for efficient effector recruitment and signal transduction. Here, we describe a super-resolution imaging method to quantify the nanometer-sized nanoclustering of RAS over a length scale between 8 and 240 nm on intact PM sheets of mammalian cells. Detailed molecular spatial distribution parameters, including the extent of nanoclustering, average cluster size, clustered fraction, and population distribution can be obtained by the univariate spatial distribution analysis. Intermolecular associations between different RAS isoforms, RAS and various PM lipids, as well as RAS and diverse effectors can be quantified via bivariate co-localization analysis.

Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH.

Advances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers.

Three dimensional cluster analysis for atom probe tomography using Ripley's K-function and machine learning.

The size and structure of spatial molecular and atomic clustering can significantly impact material properties and is therefore important to accurately quantify. Ripley's K-function (K(r)), a measure of spatial correlation, can be used to perform such quantification when the material system of interest can be represented as a marked point pattern. This work demonstrates how machine learning models based on K(r)-derived metrics can accurately estimate cluster size and intra-cluster density in simulated three dimensional (3D) point patterns containing spherical clusters of varying size; over 90% of model estimates for cluster size and intra-cluster density fall within 11% and 18% error of the true values, respectively. These K(r)-based size and density estimates are then applied to an experimental APT reconstruction to characterize MgZn clusters in a 7000 series aluminum alloy. We find that the estimates are more accurate, consistent, and robust to user interaction than estimates from the popular maximum separation algorithm. Using K(r) and machine learning to measure clustering is an accurate and repeatable way to quantify this important material attribute.

Schistosoma japonicum-infected sentinel mice: Surveillance and spatial point pattern analysis in Hubei province, China, 2010-2018.

OBJECTIVES: Progress in national schistosomiasis control in China has successfully reduced disease transmission in many districts. However, a low transmission rate hinders conventional snail surveys in identifying areas at risk. In this study, Schistosoma japonicum-infected sentinel mice surveillance was conducted to identify high-risk areas of schistosomiasis transmission in Hubei province, China. METHODS: The risk of schistosomiasis transmission was assessed using sentinel mice monitoring in Hubei province from 2010 to 2018. Field detections were undertaken in June and September, and the sentinel mice were kept for approximately 35 days in a laboratory. They were then dissected to determine whether schistosome infection was present. Ripley's K-function and kernel density estimation were applied to analyze the spatial distribution and positive point pattern of schistosomiasis transmission. RESULTS: In total, 190 sentinel mice surveillance sites were selected to detect areas of schistosomiasis infection from 2010 to 2018, with 29 (15.26%) sites showing infected mice. Of 4723 dissected mice, 256 adult worms were detected in 112 infected mice. The infection rate was 2.37%, with an average of 2.28 worms detected per infected mouse. Significantly more infected mice were detected in the June samples than in the September samples (χ2=12.11, p<0.01). Ripley's L(d) index analysis showed that, when the distance was ≤34.52km, the sentinel mice infection pattern showed aggregation, with the strongest aggregation occurring at 7.86km. Three hotspots were detected using kernel density estimation: at the junction of Jingzhou District, Gong'an County, and Shashi District in Jingzhou City; in Wuhan City at the border of the Huangpi and Dongxihu Districts, and in the Hannan and Caidian Districts. CONCLUSION: The results showed that sentinel mice surveillance is useful in identifying high-risk areas, and could provide valuable information for schistosomiasis prevention and control, especially concerning areas along the Yangtze River, such as the Fu-Lun, Dongjing-Tongshun, and Juzhang River basins.

Genetic structure at three spatial scales is consistent with limited philopatry in Ricord's Rock Iguanas (Cyclura ricordii).

Cyclura ricordii is an endemic iguana from Hispaniola Island and is threatened on the IUCN Red List. The main threats are predation by introduced mammals, habitat destruction, and hunting pressure. The present study focused on two nesting sites from Pedernales Province in the Dominican Republic. The hypothesis that natal philopatry influences dispersal and nest-site selection was tested. Monitoring and sampling took place in 2012 and 2013. Polymorphic markers were used to evaluate whether natal philopatry limits dispersal at multiple spatial scales. Ripley's K revealed that nests were significantly clustered at multiple scales, when both nesting sites were considered and within each nesting site. This suggests a patchy, nonrandom distribution of nests within nest sites. Hierarchical AMOVA revealed that nest-site aggregations did not explain a significant portion of genetic variation within nesting sites. However, a small but positive correlation between geographic and genetic distance was detected using a Mantel's test. Hence, the relationship between geographic distance and genetic distance among hatchlings within nest sites, while detectable, was not strong enough to have a marked effect on fine-scale genetic structure. Spatial and genetic data combined determined that the nesting sites included nesting females from multiple locations, and the hypothesis of "natal philopatry" was not supported because females nesting in the same cluster were no more closely related to each other than to other females from the same nesting site. These findings imply that nesting aggregations are more likely associated with cryptic habitat variables contributing to optimal nesting conditions.

[Spatial distribution patterns and association of Loropetalum chinense population in karst hills of Guilin, Southwest China]. / æ¡‚æž—å²©æº¶çŸ³å±±æªµæœ¨ç§ç¾¤ç©ºé—´æ ¼å±€åŠå ¶å ³è”æ€§.

The spatial distribution and association of populations can reflect succession patterns and its adaptation strategies to the change of environmental factors, with important significance for vegetation restoration, reconstruction, and biodiversity conservation. We used the point-pattern analytical method to examine the spatial distribution and its association of the Loropetalum chinense population in karst hills of Guilin, China, based on field surveys. On the basis of Ripley K function, we used the pair-correlation function statistic derived to explore difference in the distribution patterns.We used Ripley L function to examine the spatial associations among the three diameter classes. The results showed that the diameter class structure of the population exhibited an irregular inverted "J" type, with the small diameter class occupying a large proportion, indicating an increased population structure with good regeneration capability. The individuals of the three diameter classes had a clumped distribution at the small scale. With the increases of spatial scales, the aggregation intensity gradually weakened and tended to be randomly distributed. There was an independent spatial association between individuals among the three diameter classes at small scale. As the scale increased, the spatial association between individuals with different diameter classes became positive or negative association. The greater the difference in diameter class of L. chinense population, the weaker their spatial correlation was, which might gradually turn into negative association. Our findings contributed to a clearer understanding of the ecological strategies and scale-dependent cha-racteristics of species coexistence and underlying mechanisms during the growth and development of L. chinense population in karst hills of Guilin, providing reference for the restoration, reconstruction, protection and management of forest in karst hills.