scholarly journals LiveCellMiner: A New Tool to Analyze Mitotic Progression

2021 ◽  
Author(s):  
Daniel Moreno-Andrés ◽  
Anuk Bhattacharyya ◽  
Anja Scheufen ◽  
Johannes Stegmaier
Keyword(s):  
Software Tool ◽  
Image Features ◽  
Automated Image Analysis ◽  
Data Sets ◽  
Time Resolved ◽  
Analysis Process ◽  
Huge Data ◽  
Unbiased Manner ◽  
Major Bottleneck ◽  
Tool Set

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

Truck Weight Enforcement Measures of Effectiveness: Development and Software Application

1998 ◽  
Vol 1643 (1) ◽  
pp. 152-160 ◽  
Author(s):  
F. R. Hanscom ◽  
M. W. Goelzer
Keyword(s):  
Software Tool ◽  
Field Evaluation ◽  
Seasonal Effects ◽  
Data Sets ◽  
Activity Data ◽  
Software Application ◽  
Enforcement Effort ◽  
Measures Of Effectiveness ◽  
Truck Weight ◽  
Enforcement Activity

A software tool was developed to determine what is accomplished as the result of truck weight enforcement efforts. Traditionally applied measures (e.g., numbers of trucks weighed and citations issued) have simply provided indications of enforcement effort. These previously applied measures failed to provide results in terms of real enforcement objectives, such as deterring overweight trucks and minimizing pavement wear and tear. Consequently the need exists to develop and validate truck weight enforcement measures of effectiveness (MOE). MOEs were developed via a series of analytical procedures. They were subsequently validated in a comprehensive four-state field evaluation. Matched (weigh-in-motion) (WIM) data sets, collected under controlled baseline and enforcement conditions, were analyzed to determine the sensitivity of candidate MOEs to actual enforcement activity. Data collection conditions were controlled in order to avoid contamination from hour-of-day, day-of-week, and seasonal effects. The following MOEs, were validated on the basis of their demonstrated sensitivity to truck weight enforcement objectives and the presence of enforcement activity: (1) severity of overweight violations, (2) proportion of overweight trucks, (3) average equivalent single-axle load (ESAL), (4) excess ESALs, and (5) bridge formula violations. These measures are sensitive to legal load-limit compliance objectives of truck weight enforcement procedures as well as the potential for overweight trucks to produce pavement deterioration. The software User Guide that statistically compares calculated MOEs between observed enforcement conditions is described in this paper. The User Guide also allows users to conduct an automated pavement design life analysis estimating, the theoretical pavement-life effect resulting from the observed enforcement activity.

scholarly journals The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

2020 ◽  
Author(s):  
Daniel Overhoff ◽  
Peter Kohlmann ◽  
Alex Frydrychowicz ◽  
Sergios Gatidis ◽  
Christian Loewe ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Data ◽  
Quality Parameters ◽  
Image Features ◽  
Performance Criteria ◽  
Data Sets ◽  
Proof Of Concept ◽  
Concept Study ◽  
Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points:  Citation Format

scholarly journals Fundamental resource trade-offs for encoded distributed optimization

2020 ◽  
Author(s):  
A Salman Avestimehr ◽  
Seyed Mohammadreza Mousavi Kalan ◽  
Mahdi Soltanolkotabi
Keyword(s):  
Computational Time ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Computational Framework ◽  
Data Set ◽  
Trade Offs ◽  
Major Bottleneck ◽  
Computing Environments ◽  
Analyze Data

Abstract Dealing with the shear size and complexity of today’s massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern distributed computing environments is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. A recent computational framework, called encoded optimization, creates redundancy in the data to mitigate the effect of stragglers. In this paper, we develop novel mathematical understanding for this framework demonstrating its effectiveness in much broader settings than was previously understood. We also analyze the convergence behavior of iterative encoded optimization algorithms, allowing us to characterize fundamental trade-offs between convergence rate, size of data set, accuracy, computational load (or data redundancy) and straggler toleration in this framework.

scholarly journals Selective time-dependent changes of Cu(DPPE)(DMP)·PF6on photoexcitation

2014 ◽  
Vol 70 (a1) ◽  
pp. C776-C776 ◽  
Author(s):  
Elzbieta Trzop ◽  
Bertrand Fournier ◽  
Katarzyna Jarzembska ◽  
Jesse Sokolow ◽  
Radoslaw Kaminski ◽  
...  
Keyword(s):  
Structural Changes ◽  
Dye Sensitized Solar Cells ◽  
Department Of Energy ◽  
Data Sets ◽  
Monoclinic System ◽  
Time Resolved ◽  
Pump Probe ◽  
Light Emitting Devices ◽  
Potential Applications ◽  
Photon Source

Thanks to their potential applications as light-emitting devices, chemical sensors and dye-sensitized solar cells, heteroleptic copper (I) complexes have been extensively studied. Cu(DPPE)(DMP)·PF6(dppe= 1,2-bis(diphenylphosphino)ethane; dmp = 2,9-dimethyl-1,10-phenanthroline) crystallizes in the monoclinic system, P21/c, with two independent molecules in the asymmetric unit. Previous studies on this system [1,2] show strong temperature-dependent emission. The complex was studied at 90K under 355nm laser excitation. At this temperature, the luminescence decay for Cu(DPPE)(DMP)·PF6is biexponential with lifetimes of ~3μs and ~28μs. Two time-resolved X-ray diffraction techniques were applied for studies: (1) a Laue technique at BioCARS ID-14 beamline at the Advanced Photon Source, and (2) monochromatic diffraction at a newly constructed in-house pump-probe monochromatic facility at the University at Buffalo. Structural changes determined with the two methods are in qualitative agreement; discrepancies in position of the Cu and P atoms were observed. The molecular distortions were smaller than those determined at 16K in the earlier synchrotron study by Vorontsov et al. [2]. Photodeformation maps (see Figure below), in which the increase in temperature on photoexcitation has been eliminated, clearly illustrate the photoinduced atomic shifts for both data sets. Results will be compared with those obtained for other studied heteroleptic copper (I) complexes, for instance Cu[(1,10-phenanthroline-N,N′) bis(triphenylphosphine)]·BF4[3]. The in-house pump-probe facility is discussed by Radoslaw Kaminski at this meeting. Research funded by the National Science Foundation (CHE1213223). BioCARS Sector 14 at APS is supported by NIH (RR007707). The Advanced Photon Source is funded by the Office of Basic Energy Sciences, U.S. Department of Energy, (W-31-109-ENG-38). KNJ is supported by the Polish Ministry of Science and Higher Education through the "Mobility Plus" program.

Feature Extraction From Time Resolved Reacting Flow Data Sets

2018 ◽  
Author(s):  
H. Ek ◽  
I. Chterev ◽  
N. Rock ◽  
B. Emerson ◽  
J. Seitzman ◽  
...  
Keyword(s):  
Swirling Flow ◽  
Azimuthal Velocity ◽  
Flow Fields ◽  
Swirl Flow ◽  
Reacting Flow ◽  
Data Sets ◽  
Time Resolved ◽  
Recirculating Flow ◽  
Flow Features ◽  
Dynamical Flow

This paper presents measurements of the simultaneous fuel distribution, flame position and flow velocity in a high pressure, liquid fueled combustor. Its objective is to develop methods to process, display and compare large quantities of instantaneous data with computations. However, time-averaged flow fields rarely represent the instantaneous, dynamical flow fields in combustion systems. It is therefore important to develop methods that can algorithmically extract dynamical flow features and be directly compared between measurements and computations. While a number of data-driven approaches have been previously presented in the literature, the purpose of this paper is to propose several approaches that are based on understanding of key physical features of the flow — for this reacting swirl flow, these include the annular jet, the swirling flow which may be precessing, the recirculating flow between the annular jets, and the helical flow structures in the shear layers. This paper demonstrates nonlinear averaging of axial and azimuthal velocity profiles, which provide insights into the structure of the recirculation zone and degree of flow precession. It also presents probability fields for the location of vortex cores that enables a convenient method for comparison of their trajectory and phasing with computations. Taken together, these methods illustrate the structure and relative locations of the annular fluid jet, recirculating flow zone, spray location, flame location, and trajectory of the helical vortices.

scholarly journals Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

2019 ◽  
Vol 3 ◽  
Author(s):  
Shruthi Magesh ◽  
Viktor Jonsson ◽  
Johan Bengtsson-Palme
Keyword(s):  
Microbial Communities ◽  
Point Mutations ◽  
Software Tool ◽  
Metagenomic Data ◽  
Data Sets ◽  
Resistance Mutations ◽  
Shotgun Metagenomics ◽  
Key Factor ◽  
Detection Of Mutations ◽  
And Function

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame).

scholarly journals New processing tools for weak and/or spatially overlapped macromolecular diffraction patterns

1999 ◽  
Vol 55 (10) ◽  
pp. 1733-1741 ◽  
Author(s):  
Dominique Bourgeois
Keyword(s):  
Signal To Noise Ratio ◽  
Data Sets ◽  
Signal To Noise ◽  
Weighting Method ◽  
Time Resolved ◽  
Accurate Evaluation ◽  
Profile Fitting ◽  
Integration Program ◽  
Diffraction Patterns ◽  
New Processing

Tools originally developed for the treatment of weak and/or spatially overlapped time-resolved Laue patterns were extended to improve the processing of difficult monochromatic data sets. The integration programPrOWallows deconvolution of spatially overlapped spots which are usually rejected by standard packages. By using dynamically adjusted profile-fitting areas, a carefully built library of reference spots and interpolation of reference profiles, this program also provides a more accurate evaluation of weak spots. In addition, by using Wilson statistics, it allows rejection of non-redundant strong outliers such as zingers, which otherwise may badly corrupt the data. A weighting method for optimizing structure-factor amplitude differences, based on Bayesian statistics and originally applied to low signal-to-noise ratio time-resolved Laue data, is also shown to significantly improve other types of subtle amplitude differences, such as anomalous differences.

Construction and Analysis of Protein-Protein Interaction Network

2019 ◽  
pp. 204-220
Author(s):  
Divya Dasagrandhi ◽  
Arul Salomee Kamalabai Ravindran ◽  
Anusuyadevi Muthuswamy ◽  
Jayachandran K. S.
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Data Sets ◽  
Protein Protein Interaction ◽  
Huge Data ◽  
Associated Proteins ◽  
High Throughput Experiments ◽  
Generation Sequencing ◽  
Protein Protein Interaction Network

Understanding the mechanisms of a disease is highly complicated due to the complex pathways involved in the disease progression. Despite several decades of research, the occurrence and prognosis of the diseases is not completely understood even with high throughput experiments like DNA microarray and next-generation sequencing. This is due to challenges in analysis of huge data sets. Systems biology is one of the major divisions of bioinformatics and has laid cutting edge techniques for the better understanding of these pathways. Construction of protein-protein interaction network (PPIN) guides the modern scientists to identify vital proteins through protein-protein interaction network, which facilitates the identification of new drug target and associated proteins. The chapter is focused on PPI databases, construction of PPINs, and its analysis.

Remote Patient Monitoring for Healthcare

2019 ◽  
pp. 163-179 ◽  
Author(s):  
Andrew Stranieri ◽  
Venki Balasubramanian
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Patient Monitoring ◽  
Wearable Sensors ◽  
Data Sets ◽  
Remote Patient Monitoring ◽  
Data Mining Algorithms ◽  
Real Time Analysis ◽  
Huge Data ◽  
Remote Patient

Remote patient monitoring involves the collection of data from wearable sensors that typically requires analysis in real time. The real-time analysis of data streaming continuously to a server challenges data mining algorithms that have mostly been developed for static data residing in central repositories. Remote patient monitoring also generates huge data sets that present storage and management problems. Although virtual records of every health event throughout an individual's lifespan known as the electronic health record are rapidly emerging, few electronic records accommodate data from continuous remote patient monitoring. These factors combine to make data analytics with continuous patient data very challenging. In this chapter, benefits for data analytics inherent in the use of standards for clinical concepts for remote patient monitoring is presented. The openEHR standard that describes the way in which concepts are used in clinical practice is well suited to be adopted as the standard required to record meta-data about remote monitoring. The claim is advanced that this is likely to facilitate meaningful real time analyses with big remote patient monitoring data. The point is made by drawing on a case study involving the transmission of patient vital sign data collected from wearable sensors in an Indian hospital.

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