| 1. |
- Kirvalidze, Mariam, et al.
(author)
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Estimating pairwise overlap in umbrella reviews : Considerations for using the corrected covered area (CCA) index methodology
- 2023
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In: Research Synthesis Methods. - : John Wiley & Sons. - 1759-2879 .- 1759-2887. ; 14:5, s. 764-767
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Research review (peer-reviewed)abstract
- Umbrella reviews (reviews of systematic reviews) are increasingly used to synthesize findings from systematic reviews. One important challenge when pooling data from several systematic reviews is publication overlap, that is, the same primary publications being included in multiple reviews. Pieper et al. have proposed using the corrected covered area (CCA) index to quantify the degree of overlap between systematic reviews to be pooled in an umbrella review. Recently, this methodology has been integrated in Excel- or R-based tools for easier use. In this short letter, we highlight an important consideration for using the CCA methodology for pairwise overlap assessment, especially when reviews include varying numbers of primary publications, and we urge researchers to fine-tune this method and exercise caution when review exclusion decisions are based on its output.
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| 2. |
- Roxberg, Åsa, 1953-, et al.
(author)
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Space and place for health and care
- 2020
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In: International Journal of Qualitative Studies on Health and Well-being. - Abingdon : Taylor and Francis Ltd.. - 1748-2623 .- 1748-2631. ; 15:sup1
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Journal article (peer-reviewed)abstract
- Purpose: This discussion paper aims to contribute to a greater understanding of the state of the art of research engaged with conceptual matters of space and place for health and care. Method: The authors, who represent a variety of academic disciplines, discuss and demonstrate the conceptual recognition of space and place in research in health and caring sciences building upon own work and experience. Results: To explore the concepts of space and place for health and care is a research pursuit of utmost importance, and should be made through transdisciplinary research collaborations, whereby spatial theories from various disciplines could be communicated to cultivate truly novel and well-informed research. Furthermore, engaging with relational and topological perceptions of space and place poses methodological challenges to overcome in future research on health and care. Conclusions: We argue that there is a need for accelerating spatially informed research on health and care that is informed by current theories and perspectives on space and place, and transdisciplinary research collaborations are a means to achieving this. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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| 3. |
- Abdollahi, S., et al.
(author)
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Fermi Large Area Telescope Fourth Source Catalog
- 2020
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In: Astrophysical Journal Supplement Series. - : American Astronomical Society. - 0067-0049 .- 1538-4365. ; 247:1
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Journal article (peer-reviewed)abstract
- We present the fourth Fermi Large Area Telescope catalog (4FGL) of gamma-ray sources. Based on the first eight years of science data from the Fermi Gamma-ray Space Telescope mission in the energy range from 50 MeV to 1 TeV, it is the deepest yet in this energy range. Relative to the 3FGL catalog, the 4FGL catalog has twice as much exposure as well as a number of analysis improvements, including an updated model for the Galactic diffuse gamma-ray emission, and two sets of light curves (one-year and two-month intervals). The 4FGL catalog includes 5064 sources above 4 sigma significance, for which we provide localization and spectral properties. Seventy-five sources are modeled explicitly as spatially extended, and overall, 358 sources are considered as identified based on angular extent, periodicity, or correlated variability observed at other wavelengths. For 1336 sources, we have not found plausible counterparts at other wavelengths. More than 3130 of the identified or associated sources are active galaxies of the blazar class, and 239 are pulsars.
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| 4. |
- Abdollahi, S., et al.
(author)
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Incremental Fermi Large Area Telescope Fourth Source Catalog
- 2022
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In: Astrophysical Journal Supplement Series. - : American Astronomical Society. - 0067-0049 .- 1538-4365. ; 260:2
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Journal article (peer-reviewed)abstract
- We present an incremental version (4FGL-DR3, for Data Release 3) of the fourth Fermi Large Area Telescope (LAT) catalog of γ-ray sources. Based on the first 12 years of science data in the energy range from 50 MeV to 1 TeV, it contains 6658 sources. The analysis improves on that used for the 4FGL catalog over eight years of data: more sources are fit with curved spectra, we introduce a more robust spectral parameterization for pulsars, and we extend the spectral points to 1 TeV. The spectral parameters, spectral energy distributions, and associations are updated for all sources. Light curves are rebuilt for all sources with 1 yr intervals (not 2 month intervals). Among the 5064 original 4FGL sources, 16 were deleted, 112 are formally below the detection threshold over 12 yr (but are kept in the list), while 74 are newly associated, 10 have an improved association, and seven associations were withdrawn. Pulsars are split explicitly between young and millisecond pulsars. Pulsars and binaries newly detected in LAT sources, as well as more than 100 newly classified blazars, are reported. We add three extended sources and 1607 new point sources, mostly just above the detection threshold, among which eight are considered identified, and 699 have a plausible counterpart at other wavelengths. We discuss the degree-scale residuals to the global sky model and clusters of soft unassociated point sources close to the Galactic plane, which are possibly related to limitations of the interstellar emission model and missing extended sources.
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| 5. |
- Abdollahi, S., et al.
(author)
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Search for New Cosmic-Ray Acceleration Sites within the 4FGL Catalog Galactic Plane Sources
- 2022
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In: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 933:2
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Journal article (peer-reviewed)abstract
- Cosmic rays are mostly composed of protons accelerated to relativistic speeds. When those protons encounter interstellar material, they produce neutral pions, which in turn decay into gamma-rays. This offers a compelling way to identify the acceleration sites of protons. A characteristic hadronic spectrum, with a low-energy break around 200 MeV, was detected in the gamma-ray spectra of four supernova remnants (SNRs), IC 443, W44, W49B, and W51C, with the Fermi Large Area Telescope. This detection provided direct evidence that cosmic-ray protons are (re-)accelerated in SNRs. Here, we present a comprehensive search for low-energy spectral breaks among 311 4FGL catalog sources located within 5° from the Galactic plane. Using 8 yr of data from the Fermi Large Area Telescope between 50 MeV and 1 GeV, we find and present the spectral characteristics of 56 sources with a spectral break confirmed by a thorough study of systematic uncertainty. Our population of sources includes 13 SNRs for which the proton–proton interaction is enhanced by the dense target material; the high-mass gamma-ray binary LS I+61 303; the colliding wind binary η Carinae; and the Cygnus star-forming region. This analysis better constrains the origin of the gamma-ray emission and enlarges our view to potential new cosmic-ray acceleration sites.
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| 7. |
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| 8. |
- Aghanavesi, Somayeh, 1981-, et al.
(author)
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A multiple motion sensors index for motor state quantification in Parkinson's disease
- 2020
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In: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 189
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Journal article (peer-reviewed)abstract
- Aim: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks. Method: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients’ videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS. Results: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89. Conclusion: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results. © 2019
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| 9. |
- Aghanavesi, Somayeh, 1981-, et al.
(author)
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Motion sensor-based assessment of Parkinson's disease motor symptoms during leg agility tests : results from levodopa challenge
- 2020
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In: IEEE journal of biomedical and health informatics. - : IEEE Computer Society. - 2168-2194 .- 2168-2208. ; 24:1, s. 111-118
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Journal article (peer-reviewed)abstract
- Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.
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| 10. |
- Aghanavesi, Somayeh, 1981-
(author)
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Sensor-based knowledge- and data-driven methods : A case of Parkinson’s disease motor symptoms quantification
- 2020
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Doctoral thesis (other academic/artistic)abstract
- The overall aim of this thesis was to develop and evaluate new knowledge- and data-driven methods for supporting treatment and providing information for better assessment of Parkinson’s disease (PD).PD is complex and progressive. There is a large amount of inter- and intravariability in motor symptoms of patients with PD (PwPD). The current evaluation of motor symptoms that are done at clinics by using clinical rating scales is limited and provides only part of the health status of PwPD. An accurate and clinically approved assessment of PD is required using frequent evaluation of symptoms.To investigate the problem areas, the thesis adopted the microdata analysis approach including the stages of data collection, data processing, data analysis, and data interpretation. Sensor systems including smartphone and tri-axial motion sensors were used to collect data from advanced PwPD experimenting with repeated tests during a day. The experiments were rated by clinical experts. The data from sensors and the clinical evaluations were processed and used in subsequent analysis.The first three papers in this thesis report the results from the investigation, verification, and development of knowledge- and data-driven methods for quantifying the dexterity in PD. The smartphone-based data collected from spiral drawing and alternate tapping tests were used for the analysis. The results from the development of a smartphone-based data-driven method can be used for measuring treatment-related changes in PwPD. Results from investigation and verification of an approximate entropy-based method showed good responsiveness and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity.The next two papers, report the results from the investigation and development of motion sensor-based knowledge- and data-driven methods for quantification of the motor states in PD. The motion data were collected from experiments such as leg agility, walking, and rapid alternating movements of hands. High convergence validity resulted from using motion sensors during leg agility tests. The results of the fusion of sensor data gathered during multiple motor tests were promising and led to valid, reliable and responsive objective measures of PD motor symptoms.Results in the last paper investigating the feasibility of using the Dynamic Time-Warping method for assessment of PD motor states showed it is feasible to use this method for extracting features to be used in automatic scoring of PD motor states.The findings from the knowledge- and data-driven methodology in this thesis can be used in the development of systems for follow up of the effects of treatment and individualized treatments in PD.
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