Feature Finder Research Articles

Morphological and Functional Characterization of CD34+cell-Derived Mast Cells from Sickle Cell Anemia Patients

Introduction: Mast cells (MC) activation has been described as a key factor on several disorders, but their role on sickle cell anemia (SCA) remains unclear. SCA is characterized by chronic inflammatory events orchestrated by activated leukocytes, which contribute to vaso-occlusive crisis. Physical interplay, release or production of mediators upon MC activation could affect the response of other cells. Stable MC and eosinophils (Eos) interplay and bidirectional modulation has been described on allergic and inflammatory diseases. We aimed to determine whether MC obtained from CD34+ stem cells of SCA patients, compared to healthy volunteers (HV), show differences in phenotype, function, or ability to interact with Eos.Methods: CD34+cellswere isolated from peripheral blood (HV: 60 ml; SCA: 30 ml) and cultured in StemPro34® medium with glutamine (2mM), LDL (10 mg/ml), SCF, IL-6 and IL-3 (100 ng/ml each). Culture was weekly expanded (medium without IL-3); differentiation was confirmed by toluidine blue staining and FACS (CD117+, FceRI+). After 4 weeks, surface marker expression of MC differentiation/activation, adhesion and mast cell/eosinophil related of HV and SCA was compared by FACS. Imaging flow cytometry (IFC) was used to define nuclear morphology, to identify differences in other marker expression and to characterize in vitro physical contact between MC and Eos isolated from peripheral blood from HV and SCA individuals. Cells were separately marked with fluorescent, lipophilic stains, and Feature Finder tool was used to pinpoint frequency and remarkable features for heterotypic conjugates formation. Standardized conditions were a 1:3 MC-Eos ratio, incubation for 5 minutes at 37°C and plotting Shape Ratio and Circularity features to quantify the occurrence of MC-Eos conjugates. In addition, cells were stained with fluorescent antibodies after 2 hours of interaction to evaluate the changes in marker expression during this process.Results: There was not difference in CD34+ cell count between HV and SCA individuals (p=0.848); however, a higher number of MC was obtained from SCA samples (4th week) (HV: 1.67x105±0.40; SCA: 4.38x105±1.22; p=0.027). Mature SCA from HV (n=10) and SCA (n=10) were obtained after 5 weeks (CD117+, FceRI+>50%). FACS showed higher percentage of CD117+ cells in SCA cultures (HV 18.35±5.83%; SCA 65.25±12.28%; p<0.01). Regarding CD244 and Siglec-8, SCA samples showed higher positivity, although not statistically significant because of marked variation among individuals (CD244: HV 3.32±0.84%, SCA 16.30±10.89%; Siglec-8: HV 5.94±2.48%, SCA 8.8±5.32%, p>0.05). No difference in adhesion molecules expression was detected (data not shown). IFC analysis revealed no difference in nuclear morphology, p=0.352). Differences in CD117 and Siglec-8 expression allowed a clear separation of HV- and SCD-MC (Bkgd Mean CD117: RD 2.78; Bkgd Mean Siglec8 RD: 1.84). Heterotypical (MC-Eos) conjugates formation was a rare event (less than 5% of images); there was no difference in the percentage of conjugates (HV-MC/HV-Eos: 1.61±0.37%; HV-MC/SCA-Eos: 1.92±0.37%; SCA-MC/HVEos: 1.12±0.31%; SCA-MC/SCA-Eos: 1.26±0.27%; p>0.05). Analysis of marker expression after interaction showed that HV-MC changed CD48, CD63 and CD117 expression patterns in response to SCA-Eos challenge (Bkgd Mean RD: CD48 1.09; CD63 1.16; CD117, 1.31); similarly, SCA-MC co-cultures with SCA-Eos not only modified CD48 and CD63 (Bkgd Mean RD values: BV421-CD48 2.32; PB-CD63 2.27), but also altered CD193, in a lesser extent (Bkgd Mean CD193: RD 0.88).Conclusion: We report for the first time the morphological and functional characteristics of CD34+-derived MC obtained from samples of SCA in comparison with HV. Our data suggest that MC from SCA patients may have a different expression profile of markers related to activation even after being withdrawn from exposure to chronic inflammation and vaso-occlusion in vivo . Surface markers corresponding to CD117, CD48, CD63 and CD193 in SCA animal models could be helpful to assess the activation state of MC and their response to experimental treatments targeting these cells. DisclosuresFertrin:Alexion Pharmaceuticals: Consultancy.

Two-dimensional and multi-channel feature detection algorithm for the CALIPSO lidar measurements

Abstract. In this paper, we describe a new two-dimensional and multi-channel feature detection algorithm (2D-McDA) and demonstrate its application to lidar backscatter measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Unlike previous layer detection schemes, this context-sensitive feature finder algorithm is applied to a 2-D lidar “scene”, i.e., to the image formed by many successive lidar profiles. Features are identified when an extended and contiguous 2-D region of enhanced backscatter signal rises significantly above the expected “clear air” value. Using an iterated 2-D feature detection algorithm dramatically improves the fine details of feature shapes and can accurately identify previously undetected layers (e.g., subvisible cirrus) that are very thin vertically but horizontally persistent. Because the algorithm looks for contiguous 2-D patterns using successively lower detection thresholds, it reports strongly scattering features separately from weakly scattering features, thus potentially offering improved discrimination of juxtaposed cloud and aerosol layers. Moreover, the 2-D detection algorithm uses the backscatter signals from all available channels: 532 nm parallel, 532 nm perpendicular and 1064 nm total. Since the backscatter from some aerosol or cloud particle types can be more pronounced in one channel than another, simultaneously assessing the signals from all channels greatly improves the layer detection. For example, ice particles in subvisible cirrus strongly depolarize the lidar signal and, consequently, are easier to detect in the 532 nm perpendicular channel. Use of the 1064 nm channel greatly improves the detection of dense smoke layers, because smoke extinction at 532 nm is much larger than at 1064 nm, and hence the range-dependent reduction in lidar signals due to attenuation occurs much faster at 532 nm than at 1064 nm. Moreover, the photomultiplier tubes used at 532 nm are known to generate artifacts in an extended area below highly reflective liquid clouds, introducing false detections that artificially lower the apparent cloud base altitude, i.e., the cloud base when the cloud is transparent or the level of complete attenuation of the lidar signal when it is opaque. By adding the information available in the 1064 nm channel, this new algorithm can better identify the true apparent cloud base altitudes of such clouds.

The Herschel SPIRE Fourier Transform Spectrometer Spectral Feature Finder – V. Rotational measurements of NGC 891

ABSTRACT The ESA Herschel Spectral and Photometric Imaging Receiver (SPIRE) Fourier Transform Spectrometer (FTS) Spectral Feature Finder (FF) project is an automated spectral feature fitting routine developed within the SPIRE instrument team to extract all prominent spectral features from all publicly available SPIRE FTS observations. In this work, we demonstrate the use of the FF information extracted from three observations of the edge-on spiral galaxy NGC 891 to measure the rotation of N ii and C i gas at far-infrared frequencies in complement to radio observations of the [H i] 21-cm line and the CO(1-0) transition as well as optical measurements of Hα. We find that measurements of both N ii and C i gas follow a similar velocity profile to that of H i and Hα showing a correlation between neutral and ionized regions of the interstellar medium in the disc of NGC 891.

The Herschel SPIRE Fourier Transform Spectrometer Spectral Feature Finder I. The Spectral Feature Finder and Catalogue

ABSTRACT We provide a detailed description of the Herschel/SPIRE Fourier Transform Spectrometer (FTS) Spectral Feature Finder (FF). The FF is an automated process designed to extract significant spectral features from SPIRE FTS data products. Optimizing the number of features found in SPIRE-FTS spectra is challenging. The wide SPIRE-FTS frequency range (447–1568 GHz) leads to many molecular species and atomic fine structure lines falling within the observed bands. As the best spectral resolution of the SPIRE-FTS is ∼1.2 GHz, there can be significant line blending, depending on the source type. In order to find, both efficiently and reliably, features in spectra associated with a wide range of sources, the FF iteratively searches for peaks over a number of signal-to-noise ratio (SNR) thresholds. For each threshold, newly identified features are rigorously checked before being added to the fitting model. At the end of each iteration, the FF simultaneously fits the continuum and features found, with the resulting residual spectrum used in the next iteration. The final FF products report the frequency of the features found and the associated SNRs. Line flux determination is not included as part of the FF products, as extracting reliable line flux from SPIRE-FTS data is a complex process that requires careful evaluation and analysis of the spectra on a case-by-case basis. The FF results are 100 per cent complete for features with SNR greater than 10 and 50–70 per cent complete at SNR of 5. The FF code and all FF products are publicly available via the Herschel Science Archive.

The Herschel SPIRE Fourier Transform Spectrometer Spectral Feature Finder – III. Line identification and off-axis spectra

ABSTRACT The European Space Agency Herschel Spectral and Photometric Imaging Receiver (SPIRE) Fourier Transform Spectrometer (FTS) Spectral Feature Finder (FF) project is an automated spectral feature fitting routine developed within the SPIRE instrument team to extract all prominent spectral features from all publicly available SPIRE FTS observations. We present the extension of the FF to include the off-axis detectors of the FTS in sparsely sampled single-pointing observations, the results of which have been ingested into the catalogue. We also present the results from an automated routine for identifications of the atomic/molecular transitions that correspond to the spectral features extracted by the FF. We use a template of 307 atomic fine structure and molecular lines that are commonly found in SPIRE FTS spectra for the cross-match. The routine makes use of information provided by the line identification to search for low signal-to-noise ratio features that have been excluded or missed by the iterative FF. In total, the atomic/molecular transitions of 178 942 lines are identified (corresponding to 83 per cent of the entire FF catalogue), and an additional 33 840 spectral lines associated with missing features from SPIRE FTS observations are added to the FF catalogue.

The Herschel SPIRE Fourier Transform Spectrometer Spectral Feature Finder − II. Estimating radial velocity of SPIRE spectral observation sources

ABSTRACT The Herschel SPIRE FTS Spectral Feature Finder (FF) detects significant spectral features within SPIRE spectra and employs two routines, and external references, to estimate source radial velocity. The first routine is based on the identification of rotational 12CO emission, the second cross-correlates detected features with a line template containing most of the characteristic lines in typical far infrared observations. In this paper, we outline and validate these routines, summarize the results as they pertain to the FF, and comment on how external references were incorporated.

The Herschel SPIRE Fourier Transform Spectrometer Spectral Feature Finder – IV. Neutral carbon detection in the SPIRE FTS spectra

ABSTRACT The SPIRE Fourier Transform Spectrometer (FTS) Spectral Feature Finder (FF), developed within the Herschel Spectral and Photometric Imaging Receiver (SPIRE) FTS instrument team, is an automated spectral feature fitting routine that attempts to find significant features in SPIRE FTS spectra. The 3P1–3P0 and 3P2–3P1 neutral carbon fine structure lines are common features in carbon-rich far-infrared astrophysical sources. These features can be difficult to detect using an automated feature detection routine due to their typically low amplitude and line blending. In this paper, we describe and validate the FF subroutine designed to detect the neutral carbon emission observed in SPIRE spectral data.

Bacterial Feature Finder (BaFF)-a system for extracting features overrepresented in sets of prokaryotic organisms.

The results of some experimental and computational techniques are given in terms of large sets of organisms, especially prokaryotic. While their distinctive features can provide useful data regarding specific phenomenon, there are no automated tools for extracting them. We present here the Bacterial Feature Finder web server, a tool to automatically interrogate sets of prokaryotic organisms provided by the user to evaluate their specific biological features. At the core of the system is a searchable database of qualitative and quantitative features compiled for more than 23 000 prokaryotic organisms. Both the input set of organisms and the background set used to calculate the enriched features can be directly provided by the user, or they can be obtained by searching the database. The results are presented via an interactive graphical interface, with links to external resources. The web server is freely available at http://csbg.cnb.csic.es/BaFF. It has been tested in the main web browsers and does not require any especial plug-ins or additional software. Supplementary data are available at Bioinformatics online.

Discriminating between clouds and aerosols in the CALIOP version 4.1 data products

Abstract. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Operations (CALIPSO) mission released version 4.1 (V4) of their lidar level 2 cloud and aerosol data products in November 2016. These new products were derived from the CALIPSO V4 lidar level 1 data, in which the calibration of the measured backscatter data at both 532 and 1064 nm was significantly improved. This paper describes updates to the V4 level 2 cloud–aerosol discrimination (CAD) algorithm that more accurately differentiate between clouds and aerosols throughout the Earth's atmosphere. The level 2 data products are improved with new CAD probability density functions (PDFs) that were developed to accommodate extensive calibration changes in the level 1 data. To enable more reliable identification of aerosol layers lofted into the upper troposphere and lower stratosphere, the CAD training dataset used in the earlier data releases was expanded to include stratospheric layers and representative examples of volcanic aerosol layers. The generic “stratospheric layer” classification reported in previous versions has been eliminated in V4, and cloud–aerosol classification is now performed on all layers detected everywhere from the surface to 30 km. Cloud–aerosol classification has been further extended to layers detected at single-shot resolution, which were previously classified by default as clouds. In this paper, we describe the underlying rationale used in constructing the V4 PDFs and assess the performance of the V4 CAD algorithm in the troposphere and stratosphere. Previous misclassifications of lofted dust and smoke in the troposphere have been largely improved, and volcanic aerosol layers and aerosol layers in the stratosphere are now being properly classified. CAD performance for single-shot layer detections is also evaluated. Most of the single-shot layers classified as aerosol occur within the dust belt, as may be expected. Due to changes in the 532 nm calibration coefficients, the V4 feature finder detects ∼9.0 % more features at night and ∼2.5 % more during the day. These features are typically weakly scattering and classified about equally as clouds and aerosols. For those tropospheric layers detected in both V3 and V4, the CAD classifications of more than 95 % of all cloud and daytime aerosol layers remain unchanged, as do the classifications of ∼89 % of nighttime aerosol layers. Overall, the nighttime net cloud and aerosol fractions remain unchanged from V3 to V4, but the daytime net aerosol fraction is increased by about 2 % and the daytime net cloud fraction is decreased by about 2 %.

Aerosol optical properties retrieved from the future space lidar mission ADM-aeolus

The ADM-Aeolus mission, to be launched by end of 2017, will enable the retrieval of aerosol optical properties (extinction and backscatter coefficients essentially) for different atmospheric conditions. A newly developed feature finder (FF) algorithm enabling the detection of aerosol and cloud targets in the atmospheric scene has been implemented. Retrievals of aerosol properties at a better horizontal resolution based on the feature finder groups have shown an improvement mainly on the backscatter coefficient compared to the common 90 km product.

Bayesian network feature finder (BANFF): an R package for gene network feature selection.

Network marker selection on genome-scale networks plays an important role in the understanding of biological mechanisms and disease pathologies. Recently, a Bayesian nonparametric mixture model has been developed and successfully applied for selecting genes and gene sub-networks. Hence, extending this method to a unified approach for network-based feature selection on general large-scale networks and creating an easy-to-use software package is on demand. We extended the method and developed an R package, the Bayesian network feature finder (BANFF), providing a package of posterior inference, model comparison and graphical illustration of model fitting. The model was extended to a more general form, and a parallel computing algorithm for the Markov chain Monte Carlo -based posterior inference and an expectation maximization-based algorithm for posterior approximation were added. Based on simulation studies, we demonstrate the use of BANFF on analyzing gene expression on a protein-protein interaction network. https://cran.r-project.org/web/packages/BANFF/index.html CONTACT: jiankang@umich.edu, tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.

A new method combining enhanced resolution and pattern identification

We present a simple and general-purpose method able to combine high-resolution procedure with the classification and identification of objects of interest from microscopy imaging. The method is composed of two stages. First (pattern recognition), promising components (possible objects of interest) in the image are detected and small regions containing the objects of interest are extracted using a feature finder. Second, high-resolution algorithms are applied to such identified components in order to approach a multiple scales of resolution. Although the method is indeed to be applied to any microscopy technique, in this paper, we have focused the attention on biological systems, like animal cells, recorded with an atomic force microscopy.

LC-IMS-MS Feature Finder: detecting multidimensional liquid chromatography, ion mobility and mass spectrometry features in complex datasets

The addition of ion mobility spectrometry to liquid chromatography-mass spectrometry experiments requires new, or updated, software tools to facilitate data processing. We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension. LC-IMS-MS Feature Finder is available as a command-line tool for download at http://omics.pnl.gov/software/LC-IMS-MS_Feature_Finder.php. The Microsoft.NET Framework 4.0 is required to run the software. All other dependencies are included with the software package. Usage of this software is limited to non-profit research to use (see README). rds@pnnl.gov. Supplementary data are available at Bioinformatics online.

Automatic recognition of biological particles in microscopic images

A simple and general-purpose system to recognize biological particles is presented. It is composed of four stages: First (if necessary) promising locations in the image are detected and small regions containing interesting samples are extracted using a feature finder. Second, differential invariants of the brightness are computed at multiple scales of resolution. Third, after point-wise non-linear mappings to a higher dimensional feature space, this information is averaged over the whole region thus producing a vector of features for each sample that is invariant with respect to rotation and translation. Fourth, each sample is classified using a classifier obtained from a mixture-of-Gaussians generative model. This system was developed to classify 12 categories of particles found in human urine; it achieves a 93.2% correct classification rate in this application. It was subsequently trained and tested on a challenging set of images of airborne pollen grains where it achieved an 83% correct classification rate for the three categories found during one month of observation. Pollen classification is challenging even for human experts and this performance is considered good.

Extracting an Arabic Lexicon from Arabic Newspaper Text

We describe how to build a large comprehensive, integrated Arabic lexicon by automatic parsing of newspaper text. We have built a parser system to read Arabic newspaper articles, isolate the tokens from them, find the part of speech, and the features for each token. To achieve this goal we designed a set of algorithms, we generated several sets of rules, and we developed a set of techniques, and a set of components to carry out these techniques. As each sentence is processed, new words and features are added to the lexicon, so that it grows continuously as the system runs. To test the system we have used 100 articles (80,444 words) from the Al-Raya newspaper. The system consists of several modules: the tokenizer module to isolate the tokens, the type finder system to find the part of speech of each token, the proper noun phrase parser module to mark the proper nouns and to discover some information about them and the feature finder module to find the features of the words.

Feature recognition from CAD models

This article presents a system dubbed IF/sup 2/ (Integrated Incremental Feature Finder), which functions as a feature recognizer or a feature model converter depending on whether the input CAD data contains design features. IF/sup 2/ recognizes three types of machining features: holes, slots and pockets. This article discusses recognition algorithms for slots and pockets. Recognition algorithms for holes are similar to those of slots (Han, 1996).

Integration of feature based design and feature recognition

Process planning for machined parts typically requires that a part be described through machining features such as holes, slots and pockets. This paper presents a novel feature finder, which automatically generates a part interpretation in terms of machining features, by utilizing information from a variety of sources such as nominal geometry, tolerances and attributes, and design features. The feature finder strives to produce a desirable interpretation of the part as quickly as possible. If this interpretation is judged unacceptable by a process planner, alternatives can be generated on demand. The feature finder uses a hint-based approach, and combines artificial intelligence techniques, such as blackboard architecture and uncertain reasoning, with the geometric completion procedures first introduced in the OOFF system previously developed at USC.