Hasil untuk "Maps"

D. Cordes, V. Haughton, K. Arfanakis et al.

Nees Jan van Eck, L. Waltman, R. Dekker et al.

VOS is a new mapping technique that can serve as an alternative to the well-known technique of multidimensional scaling. We present an extensive comparison between the use of multidimensional scaling and the use of VOS for constructing bibliometric maps. In our theoretical analysis, we show the mathematical relation between the two techniques. In our experimental analysis, we use the techniques for constructing maps of authors, journals, and keywords. Two commonly used approaches to bibliometric mapping, both based on multidimensional scaling, turn out to produce maps that suffer from artifacts. Maps constructed using VOS turn out not to have this problem. We conclude that in general maps constructed using VOS provide a more satisfactory representation of a data set than maps constructed using well-known multidimensional scaling approaches.

M. Schuldiner, Sean R. Collins, Natalie J. Thompson et al.

P. Cregan, T. Jarvik, A. Bush et al.

K. Devkota, Amar Deep Regmi, H. Pourghasemi et al.

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling–Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling–Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.

Marianne E. Sinka, M. Bangs, S. Manguin et al.

BackgroundGlobal maps, in particular those based on vector distributions, have long been used to help visualise the global extent of malaria. Few, however, have been created with the support of a comprehensive and extensive evidence-based approach.MethodsHere we describe the generation of a global map of the dominant vector species (DVS) of malaria that makes use of predicted distribution maps for individual species or species complexes.ResultsOur global map highlights the spatial variability in the complexity of the vector situation. In Africa, An. gambiae, An. arabiensis and An. funestus are co-dominant across much of the continent, whereas in the Asian-Pacific region there is a highly complex situation with multi-species coexistence and variable species dominance.ConclusionsThe competence of the mapping methodology to accurately portray DVS distributions is discussed. The comprehensive and contemporary database of species-specific spatial occurrence (currently available on request) will be made directly available via the Malaria Atlas Project (MAP) website from early 2012.

Adele E. Clarke

Mohammed Okely, Ze Chen, Eslam Adly et al.

Abstract The Asian long-horned tick, Haemaphysalis longicornis Neumann, 1901, is the competent vector for severe fever with thrombocytopenia syndrome virus (SFTSV). Haemaphysalis longicornis originated mainly in eastern Asia and invaded many areas like Australia, New Zealand, and the Pacific islands, and was recently introduced to eastern parts of the USA. This species is characterized by high adaptability to a wide range of temperatures and can reproduce parthenogenically under stressful conditions. Migratory birds are important hosts of H. longicornis and are thought to be responsible for its unexpected invasion and introduction into new areas worldwide. This study predicted the historical (near current) global environmental suitability and the possible shifts in environmental suitability for H. longicornis under the ongoing climate change between 2021 and 2100. The results demonstrated that Europe is at potential of high environmental suitability for H. longicornis invasion although this species has not been recorded in any regions of Europe yet. Our model also anticipated the environmental suitability for H. longicornis in eastern parts of the USA, although the recently recorded occurrences there were not used in the model calibration. Climate change is thought to affect and increase the range of suitable environments for H. longicornis. The different maps introduced in this study may help improve understanding of the global environmental suitability for this invasive disease vector and predict the areas at high environmental suitability for possible invasion to prioritize the control programs and enhance quarantine procedures in these areas.

Giulia Bosio, Alberto Collareta, Matteo Pedini et al.

The Miocene Pisco Formation (East Pisco Basin, Peru) is renowned for its abundant, well-preserved fossils of marine vertebrates, representing one of the most spectacular and complete records of Neogene marine vertebrates worldwide. Here, we present a geological map at 1:10,000 scale investigating the spatial and temporal distribution of fossil vertebrates at Cerros Cadena de los Zanjones, in the Ica River Valley. Stratigraphic and paleontological analyses reveal the widespread occurrence of marine vertebrate remains in the Tortonian (10.0–8.6 Ma) P1 and Tortonian – Messinian (8.4–6.9 Ma) P2 sequences. These include 91 specimens preserved as bony elements, including cetaceans (both Odontoceti and Mysticeti), seals (Pinnipedia) and bony fishes (Osteichthyes). Elasmobranchs, including Carcharhiniformes, Lamniformes and Myliobatiformes, are represented by some 300 teeth. The P1-P2 passage is marked by faunal novelties such as the first appearance of seals. Overall, the assemblage taxonomically resembles that of the nearby, well-investigated site of Cerro Colorado.

Michael S Spilman

The widespread establishment of cryo-electron microscopy (cryo-EM) facilities equipped with electron-counting direct electron detectors (DEDs) offers new opportunities for their application in microED experiments. Electron counting offers significant improvements in sensitivity necessary for detecting high-resolution diffraction spots. The limited linear range of most frame-based electron-counting DEDs makes them prone to coincidence loss from the higher intensity of low-resolution reflections. Recent advancements in event-based electron counting (EBEC) technology, as demonstrated by the Direct Electron Apollo detector, significantly reduce coincidence loss and allows it to capture stronger reflections without saturating (Figure 1). Apollo uses a novel event counting monolithic active pixel (MAPS) sensor with on-chip CDS and edge computing to count, upsample and sum the data within the camera hardware enabling much higher counting rates. Initial small-molecule microED experiments using the Apollo detector of sodium glutamate and histidine resolved to a remarkable 0.5 Å (Figure 2). These high-resolution datasets were acquired approximately ten times faster than those obtained with traditional frame-based MAPS detectors. Apollo's improved sensitivity and acquisition speed streamline experimental workflows and support high-throughput crystallographic screening, significantly expanding the practical applications of MicroED in structural biology and pharmaceutical research.

Sergiy Kozerenko

Songyuan Li, Junyi Feng, Xi Li

Recent approaches for fast semantic video segmentation have reduced redundancy by warping feature maps across adjacent frames, greatly speeding up the inference phase. However, the accuracy drops seriously owing to the errors incurred by warping. In this paper, we propose a novel framework and design a simple and effective correction stage after warping. Specifically, we build a non-key-frame CNN, fusing warped context features with current spatial details. Based on the feature fusion, our context feature rectification (CFR) module learns the model’s difference from a per-frame model to correct the warped features. Furthermore, our residual-guided attention (RGA) module utilizes the residual maps in the compressed domain to help CRF focus on error-prone regions. Results on Cityscapes show that the accuracy significantly increases from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>67.3</mn><mo>%</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>71.6</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the speed edges down from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>65.5</mn></mrow></semantics></math></inline-formula> FPS to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>61.8</mn></mrow></semantics></math></inline-formula> FPS at a resolution of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1024</mn><mo>×</mo><mn>2048</mn></mrow></semantics></math></inline-formula>. For non-rigid categories, e.g., “human” and “object”, the improvements are even higher than 18 percentage points.

Osama Seidahmed, Sharon Jamea, Serah Kurumop et al.

Malaria risk in Papua New Guinea (PNG) is highly heterogeneous, between and within geographical regions, which is operationally challenging for control. To enhance targeting of malaria interventions in PNG, we investigated risk factors and stratified malaria incidence at the level of health facility catchment areas. Catchment areas and populations of 808 health facilities were delineated using a travel-time accessibility approach and linked to reported malaria cases (2011–2019). Zonal statistics tools were used to calculate average altitude and air temperature in catchment areas before they were spatially joined with incidence rates. In addition, empirical Bayesian kriging (EBK) was employed to interpolate incidence risk strata across PNG. Malaria annual incidence rates are, on average, 186.3 per 1000 population in catchment areas up to 600 m, dropped to 98.8 at (800–1400) m, and to 24.1 cases above 1400 m altitude. In areas above the two altitudinal thresholds 600m and 1400m, the average annual temperature drops below 22°C and 17°C, respectively. EBK models show very low- to low-risk strata (<100 cases per 1000) in the Highlands, National Capital District and Bougainville. In contrast, patches of high-risk (>200 per 1000) strata are modelled mainly in Momase and Islands Regions. Besides, strata with moderate risk (100–200) predominate throughout the coastal areas. While 35.7% of the PNG population (estimated 3.33 million in 2019) lives in places at high or moderate risk of malaria, 52.2% (estimated 4.88 million) resides in very low-risk areas. In five provinces, relatively large proportions of populations (> 50%) inhabit high-risk areas: New Ireland, East and West New Britain, Sandaun and Milne Bay. Incidence maps show a contrast in malaria risk between coastal and inland areas influenced by altitude. However, the risk is highly variable in low-lying areas. Malaria interventions should be guided by sub-national risk levels in PNG.

Gustavo Willam Pereira, Domingos Sárvio Magalhães Valente, Daniel Marçal de Queiroz et al.

Machine Learning (ML) algorithms have been used as an alternative to conventional and geostatistical methods in digital mapping of soil attributes. An advantage of ML algorithms is their flexibility to use various layers of information as covariates. However, ML algorithms come in many variations that can make their application by end users difficult. To fill this gap, a <i>Smart-Map</i> plugin, which complements Geographic Information System QGIS Version 3, was developed using modern artificial intelligence (AI) tools. To generate interpolated maps, Ordinary Kriging (OK) and the <i>Support Vector Machine</i> (<i>SVM</i>) algorithm were implemented. The <i>SVM</i> model can use vector and raster layers available in QGIS as covariates at the time of interpolation. Covariates in the <i>SVM</i> model were selected based on spatial correlation measured by Moran’s Index (I’Moran). To evaluate the performance of the <i>Smart-Map</i> plugin, a case study was conducted with data of soil attributes collected in an area of 75 ha, located in the central region of the state of Goiás, Brazil. Performance comparisons between OK and <i>SVM</i> were performed for sampling grids with 38, 75, and 112 sampled points. <i>R</i>² and <i>RMSE</i> were used to evaluate the performance of the methods. <i>SVM</i> was found superior to OK in the prediction of soil chemical attributes at the three sample densities tested and was therefore recommended for prediction of soil attributes. In this case study, soil attributes with <i>R</i>² values ranging from 0.05 to 0.83 and <i>RMSE</i> ranging from 0.07 to 12.01 were predicted by the methods tested.

Joe Gerlach

Enjie Ding, Yuhao Cheng, Chengcheng Xiao et al.

Light-weight convolutional neural networks (CNNs) suffer limited feature representation capabilities due to low computational budgets, resulting in degradation in performance. To make CNNs more efficient, dynamic neural networks (DyNet) have been proposed to increase the complexity of the model by using the Squeeze-and-Excitation (SE) module to adaptively obtain the importance of each convolution kernel through the attention mechanism. However, the attention mechanism in the SE network (SENet) selects all channel information for calculations, which brings essential challenges: (a) interference caused by the internal redundant information; and (b) increasing number of network calculations. To address the above problems, this work proposes a dynamic convolutional network (termed as EAM-DyNet) to reduce the number of channels in feature maps by extracting only the useful spatial information. EAM-DyNet first uses the random channel reduction and channel grouping reduction methods to remove the redundancy in the information. As the downsampling of information can lead to the loss of useful information, it then applies an adaptive average pooling method to maintain the information integrity. Extensive experimental results on the baseline demonstrate that EAM-DyNet outperformed the existing approaches, thus it can achieve higher accuracy of the network test and less network parameters.

Vanja Harsaker, Kristin Jensen, Hilde Kjernlie Andersen et al.

Abstract Background The aim of this study was to quantitatively benchmark iodine imaging across specific virtual monoenergetic energy levels, iodine maps and virtual non-contrast images with different phantom sizes and iodine concentrations, using a rapid switching dual-energy CT (DECT) and a dual source DECT, in order to investigate accuracy and potential differences between the technologies. Methods Solutions of iodine contrast (10, 20, 30, 50, and 100 mg/mL), sterile water and saline were scanned in a phantom on a rapid switching single-source and dual-source DECT scanners from two different vendors. The phantom was equipped with polyurethane rings simulating three body sizes. The datasets were reconstructed in virtual monoenergetic energy levels (70, 80, 90, 100, 110, 120, 130, and 140 keV), virtual non-contrast images and iodine maps. HU and iodine concentrations were measured by placing ROIs in the iodine solutions. Results The iodine concentrations were reproduced with a high degree of accuracy for the single-source DECT (1.8–9.0%), showing a slight dependence on phantom size. The dual source DECT technique showed deviant values (error -33.8 to 12.0%) for high concentrations. In relation to the virtual non-contrast measurements, the images from both vendors were affected by the iodine concentration and phantom size (-127.8 to 539.1 HU). Phantom size did not affect the calculated monoenergetic attenuation values, but the attenuation values varied between the scanners. Conclusions Quantitative measurements of post-processed images are dependent on the concentration of iodine, the phantom size and different technologies. However, our study indicates that the iodine maps are reliable for quantification of iodine.

Nyasha G. Maforo, Patrick Magrath, Kévin Moulin et al.

Abstract Background Cardiovascular disease is the leading cause of death in patients with Duchenne muscular dystrophy (DMD)—a fatal X-linked genetic disorder. Late gadolinium enhancement (LGE) imaging is the current gold standard for detecting myocardial tissue remodeling, but it is often a late finding. Current research aims to investigate cardiovascular magnetic resonance (CMR) biomarkers, including native (pre-contrast) T1 and extracellular volume (ECV) to evaluate the early on-set of microstructural remodeling and to grade disease severity. To date, native T1 measurements in DMD have been reported predominantly at 1.5T. This study uses 3T CMR: (1) to characterize global and regional myocardial pre-contrast T1 differences between healthy controls and LGE + and LGE− boys with DMD; and (2) to report global and regional myocardial post-contrast T1 values and myocardial ECV estimates in boys with DMD, and (3) to identify left ventricular (LV) T1-mapping biomarkers capable of distinguishing between healthy controls and boys with DMD and detecting LGE status in DMD. Methods Boys with DMD (N = 28, 13.2 ± 3.1 years) and healthy age-matched boys (N = 20, 13.4 ± 3.1 years) were prospectively enrolled and underwent a 3T CMR exam including standard functional imaging and T1 mapping using a modified Look-Locker inversion recovery (MOLLI) sequence. Pre-contrast T1 mapping was performed on all boys, but contrast was administered only to boys with DMD for post-contrast T1 and ECV mapping. Global and segmental myocardial regions of interest were contoured on mid LV T1 and ECV maps. ROI measurements were compared for pre-contrast myocardial T1 between boys with DMD and healthy controls, and for post-contrast myocardial T1 and ECV between LGE + and LGE− boys with DMD using a Wilcoxon rank-sum test. Results are reported as median and interquartile range (IQR). p-Values < 0.05 were considered significant. Receiver Operating Characteristic analysis was used to evaluate a binomial logistic classifier incorporating T1 mapping and LV function parameters in the tasks of distinguishing between healthy controls and boys with DMD, and detecting LGE status in DMD. The area under the curve is reported. Results Boys with DMD had significantly increased global native T1 [1332 (60) ms vs. 1289 (56) ms; p = 0.004] and increased within-slice standard deviation (SD) [100 (57) ms vs. 74 (27) ms; p = 0.001] compared to healthy controls. LGE− boys with DMD also demonstrated significantly increased lateral wall native T1 [1322 (68) ms vs. 1277 (58) ms; p = 0.001] compared to healthy controls. LGE + boys with DMD had decreased global myocardial post-contrast T1 [565 (113) ms vs 635 (126) ms; p = 0.04] and increased global myocardial ECV [32 (8) % vs. 28 (4) %; p = 0.02] compared to LGE− boys. In all classification tasks, T1-mapping biomarkers outperformed a conventional biomarker, LV ejection fraction. ECV was the best performing biomarker in the task of predicting LGE status (AUC = 0.95). Conclusions Boys with DMD exhibit elevated native T1 compared to healthy, sex- and age-matched controls, even in the absence of LGE. Post-contrast T1 and ECV estimates from 3T CMR are also reported here for pediatric patients with DMD for the first time and can distinguish between LGE + from LGE− boys. In all classification tasks, T1-mapping biomarkers outperform a conventional biomarker, LVEF.

Na Li, Jianli Shang, Jiming Wang et al.

The flesh color of watermelon (Citrullus lanatus) is an important fruit quality trait that helps to determine fruit attractiveness and is potentially beneficial to human health. Previous inheritance analyses determined that a single dominant gene, Yscr, produces the scarlet red flesh color rather than the coral red flesh color in watermelon. However, no genomic region or gene-based molecular markers for the locus Yscr have been reported thus far. In the present study, two high-density genetic maps and whole-genome variation detection aided by genome resequencing were first map the flesh color locus Yscr to a small region on chromosome 6 based on two independent populations derived from two scarlet red-fleshed lines and two coral red-fleshed lines. Two major quantitative trait loci located in the same genomic regions were identified in the F2 and BC1P2 populations and explained 90.36% and 75.1% of the phenotypic variation in flesh color, respectively. Based on the genetic variation in the two parental lines, newly developed PCR-based markers narrowed the Yscr region to 40 Kb. Of the five putative genes in this region, four encoded glycine-rich cell wall structural proteins, which implied that a new regulatory mechanism might occur between scarlet red- and coral red-fleshed in watermelon. Moreover, the genotypes of two newly developed InDel markers (InDel27_fc6 and InDel28_fc6) were completely consistent with the phenotypes in the F2 and BC1P2 populations and all 56 scarlet red-fleshed watermelon accessions. The results presented here provide valuable information for marker-assisted selection of flesh color breeding and the functional validation of candidate genes in watermelon.

Marc van Oostrum, Maik Müller, Fabian Klein et al.

Analysis of the cell surface proteome (surfaceome) is essential for cell classification but is technically challenging. Here the authors miniaturize and automate the Cell Surface Capture method to increase sensitivity, reproducibility and throughput, and use it to create population-specific surfaceome maps of developing mouse B cells.