Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification

The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.     

Method for Improving the Reliability of Learning Machine

Optimization of learning machine prediction has been attempted by the addition of a “width” parameter proposed by Wangen et al. Predictions were made for the production of phenazine compounds, the utilizations of d-sorbitol, of n-hexadecane, and of histamine by pseudomonads. In all but one cases examined, the “stability” of prediction was much improved when a high positive value was assigned to the parameter. A slight increase was often found in the hit-to-miss ratio, but the χ² test revealed that it was statistically insignificant.     

A Model-Based Study of Phytoplankton Condition Using Remote Sensing Data for the Western Kamchatka Shelf

Russian Journal of Marine Biology - A method for estimating the integral microalgae biomass beneath a unit surface was designed based on a mathematical model for phytoplankton vital function in the...     

Change in Image Quality According to the 3D Locations of a CBCT Phantom

A patient’s position changes in every CBCT scan despite patient alignment protocols. However, there have been studies to determine image quality differences when an object is located at the center of the field of view (FOV). To evaluate changes in the image quality of the CBCT scan according to different object positions, the image quality indexes of the Alphard 3030 (Alphard Roentgen Ind., Ltd., Kyoto, Japan) and the Rayscan Symphony (RAY Ind., Ltd., Suwon, Korea) were measured using the Quart DVT_AP phantom at the center of the FOV and 6 peripheral positions under four types of exposure conditions. Anterior, posterior, right, left, upper, and lower positions 1 cm offset from the center of the FOV were used for the peripheral positions. We evaluated and compared the voxel size, homogeneity, contrast to noise ratio (CNR), and the 10% point of the modulation transfer function (MTF10%) of the center and periphery. Because the voxel size, which is determined by the Nyquist frequency, was within tolerance, other image quality indexes were not influenced by the voxel size. For the CNR, homogeneity, and MTF10%, there were peripheral positions which showed considerable differences with statistical significance. The average difference between the center and periphery was up to 31.27% (CNR), 70.49% (homogeneity), and 13.64% (MTF10%). Homogeneity was under tolerance at some of the peripheral locations. Because the CNR, homogeneity, and MTF10% were significantly affected by positional changes of the phantom, an object’s position can influence the interpretation of follow up CBCT images. Therefore, efforts to locate the object in the same position are important.     

Using Remote Sensing to Map the Risk of Human Monkeypox Virus in the Congo Basin

Although the incidence of human monkeypox has greatly increased in Central Africa over the last decade, resources for surveillance remain extremely limited. We conducted a geospatial analysis using existing data to better inform future surveillance efforts. Using active surveillance data collected between 2005 and 2007, we identified locations in Sankuru district, Democratic Republic of Congo (DRC) where there have been one or more cases of human monkeypox. To assess what taxa constitute the main reservoirs of monkeypox, we tested whether human cases were associated with (i) rope squirrels (Funisciurus sp.), which were implicated in monkeypox outbreaks elsewhere in the DRC in the 1980s, or (ii) terrestrial rodents in the genera Cricetomys and Graphiurus, which are believed to be monkeypox reservoirs in West Africa. Results suggest that the best predictors of human monkeypox cases are proximity to dense forests and associated habitat preferred by rope squirrels. The risk of contracting monkeypox is significantly greater near sites predicted to be habitable for squirrels (OR?=?1.32; 95% CI 1.08-1.63). We recommend that semi-deciduous rainforests with oil-palm, the rope squirrel's main food source, be prioritized for monitoring.     

A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is therefore an excellent tool for multi-scale simulations.     

Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters.     

Using Satellite Remote Sensing to Estimate the Colored Dissolved Organic Matter Absorption Coefficient in Lakes

Given the importance of colored dissolved organic matter (CDOM) for the structure and function of lake ecosystems, a method that could estimate the amount of CDOM in lake waters over large geographic areas would be highly desirable. Satellite remote sensing has the potential to resolve this problem. We carried out model simulations to evaluate the suitability of different satellite sensors (Landsat, IKONOS, and the Advanced land Imager [ALI]) to map the amount of CDOM in concentration ranges that occur in boreal lakes of the Nordic countries. The results showed that the 8-bit radiometric resolution of Landsat 7 is not adequate when absorption by CDOM at 420 nm is higher than 3 m⁻¹. On the other hand, the 16-bit radiometric resolution of ALI, a prototype of the next generation of Landsat, is suitable for mapping CDOM in a wider range of concentrations. An ALI image of southern Finland was acquired on 14, July 2002 and in situ measurements were carried out in 15 lakes (18 stations). The results showed that there is a high correlation (R² = 0.84) between the 565 nm/660 nm ALI band ratio and the CDOM absorption coefficient in lakes. Analysis of 245 lakes in the acquired satellite image showed a normal distribution of CDOM concentration among the lakes. However, the size distribution of lakes was highly skewed toward small lakes, resulting in the CDOM concentration per unit lake area being skewed toward high values. We showed that remote sensing enables synoptic monitoring of the CDOM concentration in a large number of lakes and thus enables scaling up to the level of large ecosystems and biomes.     

Quantifying Histological Features of Cancer Biospecimens for Biobanking Quality Assurance Using Automated Morphometric Pattern Recognition Image Analysis Algorithms

Biorepository-supported translational research depends on high-quality, well-annotated specimens. Histopathology assessment contributes insight into how representative lesions are for research objectives. Feasibility of documenting histological proportions of tumor and stroma was studied in an effort to enhance information regarding biorepository tissue heterogeneity. Using commercially available software, unique spatial-spectral algorithms were developed for applying automated pattern recognition morphometric image analysis to quantify histologic tumor and nontumor tissue areas in biospecimen tissue sections. Measurements were acquired successfully for 75/75 (100%) lymphomas, 76/77 (98.7%) osteosarcomas, and 60/70 (85.7%) melanomas. The percentage of tissue area occupied by tumor varied among patients and tumor types and was distributed around medians of 94% [interquartile range (IQR)=14%] for lymphomas, 84% for melanomas (IQR=24%), and 39% for osteosarcomas (IQR=44%). Within-patient comparisons from a subset, including multiple individual patient specimens, revealed ≤12% median coefficient of variation (CV) for lymphomas and melanomas. Phenotypic heterogeneity of osteosarcomas resulted in 33% median CV. Uniformly applied, tumor-specific pattern recognition software permits automated tissue-feature quantification. Furthermore, dispersion analyses of area measurements across collections, as well as of multiple specimens from individual patients, support using limited tissue slices to gauge features for some tumor types. Quantitative image analysis automation is anticipated to minimize variability associated with routine biorepository pathologic evaluations and enhance biomarker discovery by helping to guide the selection of study-appropriate specimens.     

A Method of Neighbor Classes Based SVM Classification for Optical Printed Chinese Character Recognition

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.     

Digitise This! A Quick and Easy Remote Sensing Method to Monitor the Daily Extent of Dredge Plumes

Technological advancements in remote sensing and GIS have improved natural resource managers’ abilities to monitor large-scale disturbances. In a time where many processes are heading towards automation, this study has regressed to simple techniques to bridge a gap found in the advancement of technology. The near-daily monitoring of dredge plume extent is common practice using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and associated algorithms to predict the total suspended solids (TSS) concentration in the surface waters originating from floods and dredge plumes. Unfortunately, these methods cannot determine the difference between dredge plume and benthic features in shallow, clear water. This case study at Barrow Island, Western Australia, uses hand digitising to demonstrate the ability of human interpretation to determine this difference with a level of confidence and compares the method to contemporary TSS methods. Hand digitising was quick, cheap and required very little training of staff to complete. Results of ANOSIM R statistics show remote sensing derived TSS provided similar spatial results if they were thresholded to at least 3 mg L⁻¹. However, remote sensing derived TSS consistently provided false-positive readings of shallow benthic features as Plume with a threshold up to TSS of 6 mg L⁻¹, and began providing false-negatives (excluding actual plume) at a threshold as low as 4 mg L⁻¹. Semi-automated processes that estimate plume concentration and distinguish between plumes and shallow benthic features without the arbitrary nature of human interpretation would be preferred as a plume monitoring method. However, at this stage, the hand digitising method is very useful and is more accurate at determining plume boundaries over shallow benthic features and is accessible to all levels of management with basic training.     

A Quick Preparative Method for Electron Microscopy Observations of Delicate Objects Using Alginate Embedding Medium

A quick, safe method has been devised for embedding small or fragile specimens and keeping delicate structures intact. Cells or organisms to be embedded are placed in a viscous sodium alginate solution (1-2%), which is then polymerized in 100 mM calcium chloride. The resulting gel is easily dehydrated, embedded in resin and sectioned for electron microscopy. This method, the alginate gel portion of which was originally developed for the immobilization of Euglena, allows direct observation of each element of the specimens in micrographs. If desired, the alginate can be removed after sectioning by sequestration of calcium in a 20 mM solution of sodium citrate or a 10 mM solution of EGTA. Cells and organelles in the sections respond normally to standard staining procedures.     

Estimation of Soil Erosion Dynamics in the Koshi Basin Using GIS and Remote Sensing to Assess Priority Areas for Conservation

High levels of water-induced erosion in the transboundary Himalayan river basins are contributing to substantial changes in basin hydrology and inundation. Basin-wide information on erosion dynamics is needed for conservation planning, but field-based studies are limited. This study used remote sensing (RS) data and a geographic information system (GIS) to estimate the spatial distribution of soil erosion across the entire Koshi basin, to identify changes between 1990 and 2010, and to develop a conservation priority map. The revised universal soil loss equation (RUSLE) was used in an ArcGIS environment with rainfall erosivity, soil erodibility, slope length and steepness, cover-management, and support practice factors as primary parameters. The estimated annual erosion from the basin was around 40 million tonnes (40 million tonnes in 1990 and 42 million tonnes in 2010). The results were within the range of reported levels derived from isolated plot measurements and model estimates. Erosion risk was divided into eight classes from very low to extremely high and mapped to show the spatial pattern of soil erosion risk in the basin in 1990 and 2010. The erosion risk class remained unchanged between 1990 and 2010 in close to 87% of the study area, but increased over 9.0% of the area and decreased over 3.8%, indicating an overall worsening of the situation. Areas with a high and increasing risk of erosion were identified as priority areas for conservation. The study provides the first assessment of erosion dynamics at the basin level and provides a basis for identifying conservation priorities across the Koshi basin. The model has a good potential for application in similar river basins in the Himalayan region.     

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.     

A Novel Method for Assessment of Natural Killer Cell Cytotoxicity Using Image Cytometry

Natural killer (NK) cells belong to the innate arm of the immune system and though activated NK cells can modulate immune responses through the secretion of cytokines, their primary effector function is through target cell lysis. Accordingly, cytotoxicity assays are central to studying NK cell function. The ⁵¹Chromium release assay, is the “gold standard” for cytotoxicity assay, however, due to concerns over toxicity associated with the use and disposal of radioactive compounds there is a significant interest in non-radioactive methods. We have previously used the calcein release assay as a non-radioactive alternative for studying NK cell cytotoxicity. In this study, we show that the calcein release assay varies in its dynamic range for different tumor targets, and that the entrapped calcein could remain unreleased within apoptotic bodies of lysed tumor targets or incompletely released resulting in underestimation of percent specific lysis. To overcome these limitations, we developed a novel cytotoxicity assay using the Cellometer Vision Image Cytometer and compared this method to standard calcein release assay for measuring NK cell cytotoxicity. Using tumor lines K562, 721.221, and Jurkat, we demonstrate here that image cytometry shows significantly higher percent specific lysis of the target cells compared to the standard calcein release assay within the same experimental setup. Image cytometry is able to accurately analyze live target cells by excluding dimmer cells and smaller apoptotic bodies from viable target cell counts. The image cytometry-based cytotoxicity assay is a simple, direct and sensitive method and is an appealing option for routine cytotoxicity assay.     

一种基于支持向量机技术的癫痫脑电棘尖波识别方法

支持向量机是一种基于统计学习理论的新型学习机。文章提出一种基于支持向量机的癫痫脑电特征提取与识别方法,充分发挥其泛化能力强的特点,在与神经网络方法的比较中,表现出较低的漏检率和较好的鲁棒性,有深入研究的价值和良好的应用前景。     

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.     

A Method for Analysing Plant Architecture as it Relates to Fruit Quality Using Three-dimensional Computer Graphics

A method was developed for the spatial analysis of plant architectureas it relates to the within-plant variation in the physical,chemical, and postharvest characteristics of the fruit Computergraphics were used to reconstruct the architectural frameworkand spatial arrangement of the fruit in the canopy of kiwifruitvines (Actinidia deliciosa) trained on two different supportstructures An infra-red beam theodolite was used to obtain thespatial coordinates of the vines components The data files generatedby the theodolite were in turn used with software specificallywritten for the project (MAPIT—Microcomputer Aided PlantImaging Technology) to provide a 3-dimensional reconstructionof the original vines Each fruit was colour coded so that extremesin their attributes could be easily identified and accuratelylocated in the canopy of the vine Patterns were clearly discerniblefor both the pergola and T-bar trained vines The heavier fruitwere located at the apical ends of the canes, while greatersoluble solids concentrations were associated with the smallerfruit located closer to the cordon These patterns were consistentfor all of the vines examined The use of the theodolite coupledwith the computer graphics described in this paper providesa rapid and objective means of accurately describing plant architecture Computer graphics, plant architecture, spatial analysis, theodolite, three-dimensional analysis, fruit quality, Actinidia deliciosa, kiwifruit