27 research outputs found
Modified Newton’s method in the leapfrog method for mobile robot path planning
The problem of determining an optimal trajectory for an autonomous mobile robot in an environment with obstacles is considered. The Leapfrog approach is used to solve the ensuing system of equations derived from the first-order optimality conditions of the Pontryagin’s Minimum Principle. A comparison is made between a case in which the classical Newton Method and the Modified Newton Method are used in the shooting method for solving the two-point boundary value problem in the inner loop of the Leapfrog algorithm. It can be observed that with this modification there is an improvement in the convergence rate of the Leapfrog algorithm in general.http://www.springer.comseries/111562019-03-20hj2018Mathematics and Applied Mathematic
SPOT DETECTION METHODS IN FLUORESCENCE MICROSCOPY IMAGING: A REVIEW
Fluorescence microscopy imaging has become one of the essential tools used by biologists to visualize and study intracellular particles within a cell. Studying these particles is a long-term research effort in the field of microscopy image analysis, consisting of discovering the relationship between the dynamics of particles and their functions. However, biologists are faced with challenges such as the counting and tracking of these intracellular particles. To overcome the issues faced by biologists, tools which can extract the location and motion of these particles are essential. One of the most important steps in these analyses is to accurately detect particle positions in an image, termed spot detection. The detection of spots in microscopy imaging is seen as a critical step for further quantitative analysis. However, the evaluation of these microscopic images is mainly conducted manually, with automated methods becoming popular. This work presents some advances in fluorescence microscopy image analysis, focusing on the detection methods needed for quantifying the location of these spots. We review several existing detection methods in microscopy imaging, along with existing synthetic benchmark datasets and evaluation metrics
Caracterizacão da heterogeneidade espacial da paisagem utilizando parâmetros do semivariograma derivados de imagens NDVI
[EN] Assuming a relationship between landscape heterogeneity and measures of spatial
dependence by using remotely sensed data, the aim of this work was to evaluate the potential
of semivariogram parameters, derived from satellite images with different spatial resolutions,
to characterize landscape spatial heterogeneity of forested and human modified areas. The
NDVI (Normalized Difference Vegetation Index) was generated in an area of Brazilian amazon
tropical forest (1,000 km²). We selected samples (1 x 1 km) from forested and human modified
areas distributed throughout the study area, to generate the semivariogram and extract the sill
(¿²-overall spatial variability of the surface property) and range (¿-the length scale of the spatial
structures of objects) parameters. The analysis revealed that image spatial resolution influenced
the sill and range parameters. The average sill and range values increase from forested to human
modified areas and the greatest between-class variation was found for LANDSAT 8 imagery,
indicating that this image spatial resolution is the most appropriate for deriving sill and range
parameters with the intention of describing landscape spatial heterogeneity. By combining
remote sensing and geostatistical techniques, we have shown that the sill and range parameters
of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity
and can be used to provide landscape heterogeneity maps to enable researchers to design
appropriate sampling regimes. In the future, more applications combining remote sensing and
geostatistical features should be further investigated and developed, such as change detection
and image classification using object-based image analysis (OBIA) approaches.[PT] Assumindo a existência de uma relação entre a heterogeneidade da paisagem e medidas de dependência espacial obtidas de dados de sensoriamento remoto, o objetivo deste estudo foi avaliar o potencial dos parâmetros do semivariograma derivados de imagens de satélite com diferentes resoluções espaciais, para caracterizar áreas cobertas por floresta e áreas sob ação antrópica. Para isso, o NDVI (Índice de Vegetação da Diferença Normalizada) de cada umas das imagens (SPOT 6, Landsat 8 e MODIS Terra) foi gerado em uma área de floresta tropical Amazônica (1.000 km²), onde foram selecionadas amostras (1 x 1 km) de áreas florestadas e áreas antrópicas. A partir destes dados, foram gerados os semivariogramas e extraídos os parâmetros patamar (¿²-variabilidade espacial total) e alcance (¿-distância dentro da qual as amostras apresentam-se estruturadas espacialmente). A análise revelou que a resolução espacial das imagens influencia os parâmetros ¿² e ¿, apresentando significativo aumento das áreas de florestas para as áreas sob ação antrópica. A maior variação entre estas classes foi obtida com as imagens Landsat 8, indicando estas imagens, com resolução espacial de 30 metros, a mais apropriada para a obtenção dos parâmetros do semivariograma objetivando a caracterização da heterogeneidade espacial da paisagem. Combinando o sensoriamento remoto e técnicas geostatisticas, demonstrou-se que os parâmetros do semivariograma derivados de imagens NDVI podem ser utilizados como um simples indicador de heterogeneidade da paisagem, gerando mapas que permitem aos pesquisadores delinearem com maior eficácia o regime de amostragem. Outras aplicações combinando estas duas técnicas devem ser investigadas, como por exemplo a detecção de mudanças na cobertura do solo e a classificação de imagens utilizando análises orientada a objetos (OBIA).The authors are grateful to the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Department of Forest Science of the Federal University of Lavras (UFLA) and the ONF Brazil group for supporting this work.De Oliveira Silveira, EM.; De Mello, JM.; Acerbi Junior, FW.; Dos Reis, AA.; Withey, KD.; Ruiz Fernández, LÁ. (2017). Characterizing Landscape Spatial Heterogeneity Using Semivariogram Parameters Derived from NDVI Images. Cerne. 23(4):413-422. https://doi.org/10.1590/01047760201723042370S41342223
Reducing the effects of vegetation phenology on change detection in tropical seasonal biomes
Tropical seasonal biomes (TSBs), such as the savannas (Cerrado) and semi-arid woodlands (Caatinga) of Brazil, are vulnerable ecosystems to human-induced disturbances. Remote sensing can detect disturbances such as deforestation and fires, but the analysis of change detection in TSBs is affected by seasonal modifications in vegetation indices due to phenology. To reduce the effects of vegetation phenology on changes caused by deforestation and fires, we developed a novel object-based change detection method. The approach combines both the spatial and spectral domains of the normalized difference vegetation index (NDVI), using a pair of Operational Land Imager (OLI)/Landsat-8 images acquired in 2015 and 2016. We used semivariogram indices (SIs) as spatial features and descriptive statistics as spectral features (SFs). We tested the performance of the method using three machine-learning algorithms: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The results showed that the combination of spatial and spectral information improved change detection by correctly classifying areas with seasonal changes in NDVI caused by vegetation phenology and areas with NDVI changes caused by human-induced disturbances. The use of semivariogram indices reduced the effects of vegetation phenology on change detection. The performance of the classifiers was generally comparable, but the SVM presented the highest overall classification accuracy (92.27%) when using the hybrid set of NDVI-derived spectral-spatial features. From the vegetated areas, 18.71% of changes were caused by human-induced disturbances between 2015 and 2016. The method is particularly useful for TSBs where vegetation exhibits strong seasonality and regularly spaced time series of satellite images are difficult to obtain due to persistent cloud cover
Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing
Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics
Modelling aboveground biomass in forest remnants of the Brazilian Atlantic Forest using remote sensing, environmental and terrain-related data
The Brazilian Atlantic Forest, one of the most threatened tropical regions in the world, exhibits high levels of terrestrial aboveground biomass (AGB). We propose a random forest approach to model, map and assess whether public lands provide protection for AGB in the Rio Doce watershed, one of the most important watercourses of the Atlantic Forest biome. We used 188 field plots and individual and hybrid features from remote sensing, environmental and terrain-related data. The hybrid model improved the AGB prediction by reducing the root mean square error to 33.43 Mg/ha and increasing the coefficient of determination (R2) to 0.57. The total estimated AGB was 178,967,656.73 Mg, ranging from 20.40 to 167.72 Mg/ha following the seasonal precipitation pattern and anthropogenic disturbance effects. Only 5.76% of the total AGB was located on public protected lands, totalling 10,305,501 Mg, while most of the remaining AGB were located on private properties
Unseen rare tree species in southeast Brazilian forests: a species abundance distribution approach
Rarity is an important aspect of biodiversity often neglected in ecological studies. Species abundance distributions (SADs)
are useful tools to describe patterns of commonness–rarity in ecological communities. Most studies assume field observa-
tions of species relative abundances are approximately equal to their true relative abundances, thus dismissing the potential
for, and importance of unseen rare species. Here, we adopted the approach proposed by Chao et al. (Ecol, 96:1189–1201,
2015) to estimate the number and abundance of unseen species, and thus the true SADs, for tree species in 48 forest sites in
Minas Gerais state, Brazil (4 rainforests, 35 semideciduous forests, and 9 deciduous forests). Also, we assessed the correla-
tions between both unseen and rare species and sampling protocol and environment characteristics (climate, terrain, terrain
heterogeneity). We found estimated true SADs invariably had higher species richness values than observed in the surveys,
due to the increase in rare species. We estimate that up to 55.6% of tree species per site were unseen (8.5–55.6%), with an
average of 26.6%. The estimated percentage of rare species per site was between 31.9% and 72.8%, with an average of 57.78%.
We found rarity to be most strongly correlated with the percentage of unidentified trees, local terrain conditions and hetero-
geneity at site-level. Semideciduous forest and rainforest had similar higher percentages of unseen species (c. 27.2%) when
compared to deciduous forests, probably due to the relatively higher local heterogeneity of these forests, which may provide
more niches for rare species. Future studies should consider estimating true species abundances to better assess biodiversity