Jupiter As Seen By The Juno Microwave Radiometer: A Progress Report

2020 ◽  
Author(s):  
Steven Levin ◽  
Keyword(s):  
Microwave Radiometer ◽  
Progress Report ◽  
Vantage Point ◽  
Atmospheric Dynamics ◽  
Polar Orbit ◽  
Data Set ◽  
Wide Range ◽  
Solar Powered ◽  
Cloud Tops ◽  
Unique Vantage

<p>Juno is a spin-stabilized, solar-powered spacecraft in a highly eccentric 53.5-day polar orbit about Jupiter, with perijoves at about 5000 km above the cloud tops. From this unique vantage point, the Juno Microwave Radiometer (MWR) measures the radio emission in 6 channels, at wavelengths ranging from 1.4 to 50 cm, with 100 mS sampling throughout each spin of the spacecraft.  This data set covers the Jovian atmosphere over a wide range of latitudes, longitudes and emission angles, resulting in discoveries, puzzles, and fresh insights related to the distribution and concentration of ammonia and water, atmospheric dynamics, lightning, and other aspects of the atmosphere at depths as deep as 100 bars or more. We will present an overview of MWR results to date, incorporating data from 22 perijove passes.</p>

Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

2019 ◽  
Vol 16 (7) ◽  
pp. 808-817 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Aromatase Inhibitors ◽  
Direct Synthesis ◽  
3D Qsar ◽  
Chemotherapeutic Agents ◽  
Modeling Tools ◽  
Data Set ◽  
Wide Range ◽  
Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals Prediction of Wide Range Two-Dimensional Refractivity Using an IDW Interpolation Method from High-Altitude Refractivity Data of Multiple Meteorological Observatories

2021 ◽  
Vol 11 (4) ◽  
pp. 1431
Author(s):  
Sungsik Wang ◽  
Tae Heung Lim ◽  
Kyoungsoo Oh ◽  
Chulhun Seo ◽  
Hosung Choo
Keyword(s):  
High Altitude ◽  
Korean Peninsula ◽  
Interpolation Method ◽  
Atmospheric Condition ◽  
Propagation Path ◽  
Two Dimensional ◽  
Data Set ◽  
Wide Range ◽  
Atmospheric Data ◽  
Terrain Surface

This article proposes a method for the prediction of wide range two-dimensional refractivity for synthetic aperture radar (SAR) applications, using an inverse distance weighted (IDW) interpolation of high-altitude radio refractivity data from multiple meteorological observatories. The radio refractivity is extracted from an atmospheric data set of twenty meteorological observatories around the Korean Peninsula along a given altitude. Then, from the sparse refractive data, the two-dimensional regional radio refractivity of the entire Korean Peninsula is derived using the IDW interpolation, in consideration of the curvature of the Earth. The refractivities of the four seasons in 2019 are derived at the locations of seven meteorological observatories within the Korean Peninsula, using the refractivity data from the other nineteen observatories. The atmospheric refractivities on 15 February 2019 are then evaluated across the entire Korean Peninsula, using the atmospheric data collected from the twenty meteorological observatories. We found that the proposed IDW interpolation has the lowest average, the lowest average root-mean-square error (RMSE) of ∇M (gradient of M), and more continuous results than other methods. To compare the resulting IDW refractivity interpolation for airborne SAR applications, all the propagation path losses across Pohang and Heuksando are obtained using the standard atmospheric condition of ∇M = 118 and the observation-based interpolated atmospheric conditions on 15 February 2019. On the terrain surface ranging from 90 km to 190 km, the average path losses in the standard and derived conditions are 179.7 dB and 182.1 dB, respectively. Finally, based on the air-to-ground scenario in the SAR application, two-dimensional illuminated field intensities on the terrain surface are illustrated.

Prodromal Dementia With Lewy Bodies: Evolution of Symptoms and Predictors of Dementia Onset

2021 ◽  
pp. 089198872110235
Author(s):  
Kathryn A. Wyman-Chick ◽  
Lauren R. O’Keefe ◽  
Daniel Weintraub ◽  
Melissa J. Armstrong ◽  
Michael Rosenbloom ◽  
...  
Keyword(s):  
Clinical Features ◽  
Dementia With Lewy Bodies ◽  
Clinical Symptoms ◽  
Neuropsychiatric Symptoms ◽  
Lewy Bodies ◽  
Rem Sleep Behavior Disorder ◽  
Data Set ◽  
Wide Range ◽  
Prodromal Dementia ◽  
Dementia Onset

Background: Research criteria for prodromal dementia with Lewy bodies (DLB) were published in 2020, but little is known regarding prodromal DLB in clinical settings. Methods: We identified non-demented participants without neurodegenerative disease from the National Alzheimer’s Coordinating Center Uniform Data Set who converted to DLB at a subsequent visit. Prevalence of neuropsychiatric and motor symptoms were examined up to 5 years prior to DLB diagnosis. Results: The sample included 116 participants clinically diagnosed with DLB and 348 age and sex-matched (1:3) Healthy Controls. Motor slowing was present in approximately 70% of participants 3 years prior to DLB diagnosis. In the prodromal phase, 50% of DLB participants demonstrated gait disorder, 70% had rigidity, 20% endorsed visual hallucinations, and over 50% of participants endorsed REM sleep behavior disorder. Apathy, depression, and anxiety were common prodromal neuropsychiatric symptoms. The presence of 1+ core clinical features of DLB in combination with apathy, depression, or anxiety resulted in the greatest AUC (0.815; 95% CI: 0.767, 0.865) for distinguishing HC from prodromal DLB 1 year prior to diagnosis. The presence of 2+ core clinical features was also accurate in differentiating between groups (AUC = 0.806; 95% CI: 0.756, 0.855). Conclusion: A wide range of motor, neuropsychiatric and other core clinical symptoms are common in prodromal DLB. A combination of core clinical features, neuropsychiatric symptoms and cognitive impairment can accurately differentiate DLB from normal aging prior to dementia onset.

Relationship Between Gas Emissions and Dry Matter Intake in Mature Angus Cows

2021 ◽  
Vol 99 (Supplement_2) ◽  
pp. 32-33
Author(s):  
Amanda Holder ◽  
Megan A Gross ◽  
Alexi Moehlenpah ◽  
Paul Beck
Keyword(s):  
Dry Matter ◽  
Significant Positive Correlation ◽  
Crossover Design ◽  
Dry Matter Intake ◽  
Data Set ◽  
Gas Emissions ◽  
Positive Correlation ◽  
Wide Range ◽  
Carbon Dioxide Co2 ◽  
Second Period

Abstract The objective of this study was to examine the effects of diet quality on greenhouse gas emissions and dry matter intake (DMI). We used 42 mature, gestating Angus cows (600±69 kg; and BSC 5.3±1.1) with a wide range in DMI EPD (-1.36 to 2.29). Cows were randomly assigned to 2 diet sequences forage-concentrate (FC) or concentrate-forage(CF) determined by the diet they consumed in each period (forage or concentrate). The cows were adapted to the diet and the SmartFeed individual intake units for 14 d followed by 45 d of intake data collection for each period. Body weight was recorded on consecutive weigh days at the beginning and end of each period and then once every two wk for the duration of a period. Cows were exposed to the GreenFeed Emission Monitoring (GEM) system for no less than 9 d during each period. The GEM system was used to measure emissions of carbon dioxide (CO2) and methane (CH4). Only cows with a minimum of 20 total >3-m visits to the GEM were included in the data set. Data were analyzed in a crossover design using GLIMMIX in SASv.9.4. Within the CF sequence there was a significant, positive correlation between TMR DMI and CH4 (r=0.81) and TMR DMI and CO2 (r=0.69), however, gas emissions during the second period on the hay diet were not correlated with hay intake. There was a significant, positive correlation between hay DMI and CO2 (r=0.76) and hay DMI and CH4 (r=0.74) when cows first consumed forage (FC). In comparison to the CF sequence, cows on the FC sequence showed a positive correlation between CO2 and TMR DMI during the second period. There was also a significant positive correlation between hay and TMR DMI when assessed across (r=0.43) or within sequence (FC r=0.41, CF r=0.47).

Query Processing of RFID Data with Object Trajectory in Manufacture and Logistics

2009 ◽  
Vol 16-19 ◽  
pp. 1043-1047
Author(s):  
Sun Wei ◽  
Li Hua Dong ◽  
Yao Hua Dong
Keyword(s):  
Pervasive Computing ◽  
Radio Frequency Identification ◽  
Object Identification ◽  
Data Set ◽  
Wide Range ◽  
Storage Scheme ◽  
Rfid Data ◽  
Frequency Identification ◽  
Application Data ◽  
Path Queries

In the domain of manufacture and logistics, Radio Frequency Identification (RFID) holds the promise of real-time identifying, locating, tracking and monitoring physical objects without line of sight due to an enhanced efficiency, accuracy, and preciseness of object identification, and can be used for a wide range of pervasive computing applications. To achieve these goals, RFID data has to be collected, filtered, and transformed into semantic application data. However, the amount of RFID data is huge. Therefore, it requires much time to extract valuable information from RFID data for object tracing. This paper specifically explores options for modeling and utilizing RFID data set by XML-encoding for tracking queries and path oriented queries. We then propose a method which translates the queries to SQL queries. Based on the XML-encoding scheme, we devise a storage scheme to process tracking queries and path oriented queries efficiently. Finally, we realize the method by programming in a software system for manufacture and logistics laboratory. The system shows that our approach can process the tracing or path queries efficiently.

scholarly journals A Visual and VAE Based Hierarchical Indoor Localization Method

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3406
Author(s):  
Jie Jiang ◽  
Yin Zou ◽  
Lidong Chen ◽  
Yujie Fang
Keyword(s):  
Image Retrieval ◽  
Indoor Localization ◽  
Data Sets ◽  
Indoor Environments ◽  
Global Features ◽  
Data Set ◽  
Data Annotation ◽  
Wide Range ◽  
Annotation Costs ◽  
Global And Local

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

A generally applicable pedotransfer function that estimates field capacity and permanent wilting point from soil texture and bulk density

2008 ◽  
Vol 88 (5) ◽  
pp. 761-774 ◽  
Author(s):  
J. A. P. Pollacco
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Bulk Density ◽  
Soil Texture ◽  
The United States ◽  
Pedotransfer Functions ◽  
Pedotransfer Function ◽  
Data Set ◽  
Wide Range ◽  
Fitting Parameters

Hydrological models require the determination of fitting parameters that are tedious and time consuming to acquire. A rapid alternative method of estimating the fitting parameters is to use pedotransfer functions. This paper proposes a reliable method to estimate soil moisture at -33 and -1500 kPa from soil texture and bulk density. This method reduces the saturated moisture content by multiplying it with two non-linear functions depending on sand and clay contents. The novel pedotransfer function has no restrictions on the range of the texture predictors and gives reasonable predictions for soils with bulk density that varies from 0.25 to 2.16 g cm-3. These pedotransfer functions require only five parameters for each pressure head. It is generally accepted that the introduction of organic matter as a predictor improves the outcomes; however it was found by using a porosity based pedotransfer model, using organic matter as a predictor only modestly improves the accuracy. The model was developed employing 18 559 samples from the IGBP-DIS soil data set for pedotransfer function development (Data and Information System of the International Geosphere Biosphere Programme) database that embodies all major soils across the United States of America. The function is reliable and performs well for a wide range of soils occurring in very dry to very wet climates. Climatical grouping of the IGBP-DIS soils was proposed (aquic, tropical, cryic, aridic), but the results show that only tropical soils require specific grouping. Among many other different non-climatical soil groups tested, only humic and vitric soils were found to require specific grouping. The reliability of the pedotransfer function was further demonstrated with an independent database from Northern Italy having heterogeneous soils, and was found to be comparable or better than the accuracy of other pedotransfer functions found in the literature. Key words: Pedotransfer functions, soil moisture, soil texture, bulk density, organic matter, grouping

scholarly journals Product formalisms for measures on spaces with binary tree structures: representation, visualization, and multiscale noise

2020 ◽  
Vol 8 ◽  
Author(s):  
Devasis Bassu ◽  
Peter W. Jones ◽  
Linda Ness ◽  
David Shallcross
Keyword(s):  
Data Science ◽  
Theoretical Foundation ◽  
Continuous Functions ◽  
Noise Model ◽  
Data Types ◽  
Data Set ◽  
Tree Structures ◽  
Simple Concept ◽  
Wide Range ◽  
Computer Scientists

Abstract In this paper, we present a theoretical foundation for a representation of a data set as a measure in a very large hierarchically parametrized family of positive measures, whose parameters can be computed explicitly (rather than estimated by optimization), and illustrate its applicability to a wide range of data types. The preprocessing step then consists of representing data sets as simple measures. The theoretical foundation consists of a dyadic product formula representation lemma, and a visualization theorem. We also define an additive multiscale noise model that can be used to sample from dyadic measures and a more general multiplicative multiscale noise model that can be used to perturb continuous functions, Borel measures, and dyadic measures. The first two results are based on theorems in [15, 3, 1]. The representation uses the very simple concept of a dyadic tree and hence is widely applicable, easily understood, and easily computed. Since the data sample is represented as a measure, subsequent analysis can exploit statistical and measure theoretic concepts and theories. Because the representation uses the very simple concept of a dyadic tree defined on the universe of a data set, and the parameters are simply and explicitly computable and easily interpretable and visualizable, we hope that this approach will be broadly useful to mathematicians, statisticians, and computer scientists who are intrigued by or involved in data science, including its mathematical foundations.

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