Keyword search (4,163 papers available)

"Networks" Keyword-tagged Publications:

Title Authors PubMed ID
1 Tuning Deep Learning for Predicting Aluminum Prices Under Different Sampling: Bayesian Optimization Versus Random Search Alicia Estefania Antonio Figueroa 41751647
CONCORDIA
2 Capacitive bimetallic redox cycles and ligand-to-metal charge transfer to Boost denitrification with Ni sup II /sup /Fe sup II /sup -Gallic acid phenolic networks Yu S; Jin Y; Guo T; Li H; Liu W; Chen Z; Wang X; Guo J; 41707775
ENCS
3 Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm Lahmiri S; Tadj C; Gargour C; 41294952
ENCS
4 Exploring Deep Magnetoencephalography via Thalamo-Cortical Sleep Spindles Rattray GF; Jourde HR; Baillet S; Coffey EBJ; 41002111
PSYCHOLOGY
5 Cooperative Schemes for Joint Latency and Energy Consumption Minimization in UAV-MEC Networks Cheng M; He S; Pan Y; Lin M; Zhu WP; 40942666
ENCS
6 Efficient neural encoding as revealed by bilingualism Moore C; Donhauser PW; Klein D; Byers-Heinlein K; 40828024
PSYCHOLOGY
7 Personalizing brain stimulation: continual learning for sleep spindle detection Sobral M; Jourde HR; Marjani Bajestani SE; Coffey EBJ; Beltrame G; 40609549
PSYCHOLOGY
8 Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting Ahmed U; Mahmood A; Khan AR; Kuhlmann L; Alimgeer KS; Razzaq S; Aziz I; Hammad A; 40185800
PHYSICS
9 Large language models deconstruct the clinical intuition behind diagnosing autism Stanley J; Rabot E; Reddy S; Belilovsky E; Mottron L; Bzdok D; 40147442
ENCS
10 Psychosocial Function in Mild Cognitive Impairment: Social Participation is Associated With Cognitive Performance in Multiple Domains Rehan S; Phillips NA; 39773214
CONCORDIA
11 Asymmetric autocatalytic reactions and their stationary distribution Gallinger C; Popovic L; 39679357
MATHSTATS
12 MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle McKay MJ; Weber KA; Wesselink EO; Smith ZA; Abbott R; Anderson DB; Ashton-James CE; Atyeo J; Beach AJ; Burns J; Clarke S; Collins NJ; Coppieters MW; Cornwall J; Crawford RJ; De Martino E; Dunn AG; Eyles JP; Feng HJ; Fortin M; Franettovich Smith MM; Galloway G; Gandomkar Z; Glastras S; Henderson LA; Hides JA; Hiller CE; Hilmer SN; Hoggarth MA; Kim B; Lal N; LaPorta L; Magnussen JS; Maloney S; March L; Nackley AG; O' Leary SP; Peolsson A; Perraton Z; Pool-Goudzwaard AL; Schnitzler M; Seitz AL; Semciw AI; Sheard PW; Smith AC; Snodgrass SJ; Sullivan J; Tran V; Valentin S; Walton DM; Wishart LR; Elliott JM; 39590726
HKAP
13 A protocol for trustworthy EEG decoding with neural networks Borra D; Magosso E; Ravanelli M; 39549492
ENCS
14 Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks Adcock B; Brugiapaglia S; Dexter N; Moraga S; 39454372
MATHSTATS
15 Deep neural network-based robotic visual servoing for satellite target tracking Ghiasvand S; Xie WF; Mohebbi A; 39440297
ENCS
16 Generalization limits of Graph Neural Networks in identity effects learning D' Inverno GA; Brugiapaglia S; Ravanelli M; 39426036
ENCS
17 Cell Fate Dynamics Reconstruction Identifies TPT1 and PTPRZ1 Feedback Loops as Master Regulators of Differentiation in Pediatric Glioblastoma-Immune Cell Networks Abicumaran Uthamacumaran 39420135
PSYCHOLOGY
18 A Survey on Error Exponents in Distributed Hypothesis Testing: Connections with Information Theory, Interpretations, and Applications Espinosa S; Silva JF; Céspedes S; 39056958
ENCS
19 Social network dynamics, infant loss, and gut microbiota composition in female Colobus vellerosus during time periods with alpha male challenges Samartino S; Christie D; Penna A; Sicotte P; Ting N; Wikberg E; 38735025
BIOLOGY
20 The immunomodulatory effect of oral NaHCO3 is mediated by the splenic nerve: multivariate impact revealed by artificial neural networks Alvarez MR; Alkaissi H; Rieger AM; Esber GR; Acosta ME; Stephenson SI; Maurice AV; Valencia LMR; Roman CA; Alarcon JM; 38549144
CSBN
21 CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets Islam M; Zunair H; Mohammed N; 38492455
ENCS
22 The role of frailty in the relationships between social relationships and health outcomes: a longitudinal study Fereshteh Mehrabi 38402184
PSYCHOLOGY
23 Heterogeneous dispersal networks to improve biodiversity science Savary P; Lessard JP; Peres-Neto PR; 37891075
BIOLOGY
24 Energy scheduling for DoS attack over multi-hop networks: Deep reinforcement learning approach Yang L; Tao J; Liu YH; Xu Y; Su CY; 36848827
ENCS
25 Mutualistic coevolution and community diversity favour persistence in metacommunities under environmental changes Cosmo LG; Sales LP; Guimarães PR; Pires MM; 36629106
BIOLOGY
26 Reinforcement learning for automatic quadrilateral mesh generation: A soft actor-critic approach Pan J; Huang J; Cheng G; Zeng Y; 36375347
ENCS
27 Cancer Survivors' Evolving Perceptions of a New Supportive Virtual Program Robb A; Brown TL; Durand A; Loiselle CG; 36354724
PSYCHOLOGY
28 A Review of Mathematical and Computational Methods in Cancer Dynamics Uthamacumaran A; Zenil H; 35957879
PHYSICS
29 Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics Abicumaran Uthamacumaran 35678918
PHYSICS
30 Celebrity Couples as Business Families: A Social Network Perspective Gorji Y; Carney M; Prakash R; 34931108
CONCORDIA
31 Comment on the article "Spatially-extended nucleation-aggregation-fragmentation models for the dynamics of prion-like neurodegenerative protein-spreading in the brain and its connectome 486 (2020) 110102" Arsalan Rahimabadi 34843739
PERFORM
32 Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer Ocampo I; López RR; Camacho-León S; Nerguizian V; Stiharu I; 34683215
ENCS
33 Dynamic Covalent Polyurethane Network Materials: Synthesis and Self-healability Nellepalli P; Patel T; Oh JK; 34418209
CHEMBIOCHEM
34 COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Babaki Fard F; Samimi K; Atashzar SF; Oikonomou A; Plataniotis KN; Mohammadi A; 34113843
ENCS
35 X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech Jeancolas L; Petrovska-Delacrétaz D; Mangone G; Benkelfat BE; Corvol JC; Vidailhet M; Lehéricy S; Benali H; 33679361
PERFORM
36 The Epistemology of Evolutionary Psychology Offers a Rapprochement to Cultural Psychology Gad Saad 33224071
JMSB
37 Metabolic networks of the human gut microbiota. Selber-Hnatiw S, Sultana T, Tse W, Abdollahi N, Abdullah S, Al Rahbani J, Alazar D, Alrumhein NJ, Aprikian S, Arshad R, Azuelos JD, Bernadotte D, Beswick N, Chazbey H, Church K, Ciubotaru E, D'Amato L, Del Corpo T, Deng J, Di Giulio BL, Diveeva D, Elahie E, Frank JGM, Furze E, Garner R, Gibbs V, Goldberg-Hall R, Goldman CJ, Goltsios FF, Gorjipour K, Grant T, Greco B, Guliyev N, Habrich A, Hyland H, Ibrahim N, Iozzo T, Jawaheer-Fenaoui A, Jaworski JJ, Jhajj MK, Jones J, Joyette R, Kaudeer S, Kelley S, Ki 31799915
BIOLOGY
38 Maternal Knowing and Social Networks: Understanding First-Time Mothers' Search for Information and Support Through Online and Offline Social Networks. Price SL, Aston M, Monaghan J, Sim M, Tomblin Murphy G, Etowa J, Pickles M, Hunter A, Little V 29281945
CONCORDIA

 

Title:MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle
Authors:McKay MJWeber KAWesselink EOSmith ZAAbbott RAnderson DBAshton-James CEAtyeo JBeach AJBurns JClarke SCollins NJCoppieters MWCornwall JCrawford RJDe Martino EDunn AGEyles JPFeng HJFortin MFranettovich Smith MMGalloway GGandomkar ZGlastras SHenderson LAHides JAHiller CEHilmer SNHoggarth MAKim BLal NLaPorta LMagnussen JSMaloney SMarch LNackley AGO'Leary SPPeolsson APerraton ZPool-Goudzwaard ALSchnitzler MSeitz ALSemciw AISheard PWSmith ACSnodgrass SJSullivan JTran VValentin SWalton DMWishart LRElliott JM
Link:https://pubmed.ncbi.nlm.nih.gov/39590726/
DOI:10.3390/jimaging10110262
Publication:Journal of imaging
Keywords:MR imagingartificial intelligencemachine learningmuscle fat infiltrationneural networksnormative reference datapublic datasets
PMID:39590726 Category: Date Added:2024-11-26
Dept Affiliation: HKAP
1 Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.
2 Division of Pain Medicine, Stanford University School of Medicine, Stanford University, Stanford, CA 94304, USA.
3 Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences-Program Musculoskeletal Health, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands.
4 Department of Rehabilitation Medicine, University of Oklahoma, Norman, OK 73019, USA.
5 Department of Rehabilitation Medicine, University of Minnesota, Minneapolis, MN 55455, USA.
6 Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia.
7 Disability Prevention Program, Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
8 School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, 4072 QLD, Australia.
9 School of Health Sciences and Social Work, Griffith University, Brisbane, QLD 4111, Australia.
10 Otago Medical School, University of Otago, Dunedin 9016, New Zealand.
11 Faculty of Health Sciences, Curtin University, Perth, WA 6845, Australia.
12 Department of Health Science and Technology, Aalborg University, Gistrup, 9260 North Jutland, Denmark.
13 Northern Sydney Local Health District, The Kolling Institute, St Leonards, NSW 2065, Australia.
14 Department of Health, Kinesiology & Applied Physiology, Concordia University, Montreal, QC H4B 1R6, Canada.
15 Herston Imaging Research Facility, University of Queensland, Brisbane, QLD 4072, Australia.
16 Department of Physical Therapy, North Central College, Naperville, IL 60540, USA.
17 School of Rehabilitative and Health Sciences, Regis University, Denver, CO 80221, USA.
18 Center for Translational Pain Medicine, Department of Anesthesiology, School of Medicine, Duke University, Durham, NC 27710, USA.
19 Occupational and Environmental Medicine Centre, Department of Health Medicine and Caring Sciences, Unit of Clinical Medicine, Linköping University, 58183 Linköping, Sweden.
20 Department of Health Medicine and Caring Sciences, Unit of Physiotherapy, Linköping University, 58183 Linköping, Sweden.
21 School of Allied Health, La Trobe University, Melbourne, VIC 3086, Australia.
22 Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
23 School of Medicine, University of Colorado, Aurora, CO 80045, USA.
24 Discipline of Physiotherapy, University of Newcastle, Callaghan, NSW 2308, Australia.
25 Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia.
26 School of Health & Social Care, Edinburgh Napier University, Edinburgh, Scotland EH11 4BN, UK.
27 School of Physical Therapy, Western University, London, ON N6A 3K7, Canada.
28 School of Medicine and Dentistry, Griffith University, Brisbane, QLD 4111, Australia.

Description:

Disorders affecting the neurological and musculoskeletal systems represent international health priorities. A significant impediment to progress in trials of new therapies is the absence of responsive, objective, and valid outcome measures sensitive to early disease changes. A key finding in individuals with neuromuscular and musculoskeletal disorders is the compositional changes to muscles, evinced by the expression of fatty infiltrates. Quantification of skeletal muscle composition by MRI has emerged as a sensitive marker for the severity of these disorders; however, little is known about the composition of healthy muscles across the lifespan. Knowledge of what is 'typical' age-related muscle composition is essential to accurately identify and evaluate what is 'atypical'. This innovative project, known as the MuscleMap, will achieve the first important steps towards establishing a world-first, normative reference MRI dataset of skeletal muscle composition with the potential to provide valuable insights into various diseases and disorders, ultimately improving patient care and advancing research in the field.





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