|
1.
|
|
|
Global spectra of plant litter carbon, nitrogen and phosphorus concentrations and returning amounts
/
Yuan, Ji (Fujian Normal University. School of Geographical Sciences) ;
Wu, Fuzhong (Fujian Normal University. School of Geographical Sciences) ;
Peng, Changhui (University of Quebec. Institute of Environment Sciences) ;
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals) ;
Vallicrosa Pou, Helena (Centre de Recerca Ecològica i d'Aplicacions Forestals) ;
Sardans i Galobart, Jordi (Centre de Recerca Ecològica i d'Aplicacions Forestals) ;
Peng, Yan (Fujian Normal University. School of Geographical Sciences) ;
Wu, Qiqian (Zhejiang A & F University. State Key Laboratory of Subtropical Silviculture) ;
Li, Zimin (Université catholique de Louvain. Earth and Life Institute) ;
Heděnec, Petr (University Malaysia Terengganu. Institute of Tropical Biodiversity and Sustainable Development) ;
Li, Zhijie (Forschungszentrum Jülich GmbH. Institute of Bio- and Geosciences) ;
Tan, Siyi (Fujian Normal University. School of Geographical Sciences) ;
Yuan, Chaoxiang (Fujian Normal University. School of Geographical Sciences) ;
Ni, Xiangyin (Fujian Normal University. School of Geographical Sciences) ;
Yue, Kai (Fujian Normal University. School of Geographical Sciences)
Litter decomposition is a key ecological process that determines carbon (C) and nutrient cycling in terrestrial ecosystems. The initial concentrations of C and nutrients in litter play a critical role in this process, yet the global patterns of litter initial concentrations of C, nitrogen (N) and phosphorus (P) are poorly understood. [...]
2024 -  10.1111/1365-2745.14250
Journal of Ecology, Vol. 112, issue 4 (April 2024) , p. 717-729
|
|
|
2.
|
|
10 p, 859.5 KB |
Predicting AT(N) pathologies in Alzheimer's disease from blood-based proteomic data using neural networks
/
Zhang, Y. (University of Oxford) ;
Ghose, U. (University of Oxford) ;
Buckley, N.J. (University of Oxford) ;
Engelborghs, S. (Vrije Universiteit Brussel) ;
Sleegers, K. (University of Antwerp) ;
Frisoni, G.B. (Department of Psychiatry. University of Geneva) ;
Wallin, A. (University of Gothenburg) ;
Lleó, Alberto (Institut d'Investigació Biomèdica Sant Pau) ;
Popp, J. (University of Zürich) ;
Martinez-Lage, P. (CITA-Alzheimer Foundation) ;
Legido-Quigley, C. (Steno Diabetes Center) ;
Barkhof, F. (University College London) ;
Zetterberg, H. (Kong Kong Center for Neurodegenerative Diseases) ;
Visser, P.J. (VU University Medical Center) ;
Bertram, L. (University of Oslo) ;
Lovestone, S. (Janssen RD) ;
Nevado-Holgado, Alejo (University of Oxford) ;
Shi, L. (University of Oxford) ;
Universitat Autònoma de Barcelona
Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. [...]
2022 - 10.3389/fnagi.2022.1040001
Frontiers in aging neuroscience, Vol. 14 (29 2022) , p. 1040001
|
|
|
3.
|
|
|
Classification of suicidal thoughts and behaviour in children : results from penalised logistic regression analyses in the Adolescent Brain Cognitive Development study
/
Van Velzen, L.S. (University of Melbourne) ;
Toenders, Y.J. (Orygen) ;
Avila-Parcet, Aina (Institut d'Investigació Biomèdica Sant Pau) ;
Dinga, R. (Radboud University) ;
Rabinowitz, J.A. (Johns Hopkins University) ;
Campos, A.I. (The University of Queensland) ;
Jahanshad, N. (University of Southern California) ;
Rentería, M.E. (The University of Queensland) ;
Schmaal, L. (Orygen) ;
Universitat Autònoma de Barcelona
Despite efforts to predict suicide risk in children, the ability to reliably identify who will engage in suicide thoughts or behaviours has remained unsuccessful. Aims We apply a novel machine-learning approach and examine whether children with suicide thoughts or behaviours could be differentiated from children without suicide thoughts or behaviours based on a combination of traditional (sociodemographic, physical health, social-environmental, clinical psychiatric) risk factors, but also more novel risk factors (cognitive, neuroimaging and genetic characteristics). [...]
2022 - 10.1192/bjp.2022.7
The British journal of psychiatry, Vol. 220 Núm. 4 (february 2022) , p. 210-218
|
|
|
4.
|
|
16 p, 1.2 MB |
The problem of institutional fit : uncovering patterns with boosted decision trees
/
Epstein, Graham (University of Waterloo. School of Environment, Resources and Sustainability) ;
Apetrei, Cristina I. (Leuphana University, Faculty of Sustainability) ;
Baggio, Jacopo (University of Central Florida. School of Politics, Security and International Affairs) ;
Chawla, Sivee (James Cook University. ARC Centre of Excellence for Coral Reef Studies) ;
Cumming, Graeme S. (James Cook University. ARC Centre of Excellence for Coral Reef Studies) ;
Gurney, Georgina (James Cook University. ARC Centre of Excellence for Coral Reef Studies) ;
Morrison, Tiffany (James Cook University. ARC Centre of Excellence for Coral Reef Studies) ;
Unnikrishnan, Hita (Sheffield University. Urban Institute) ;
Villamayor Tomás, Sergio (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
Complex social-ecological contexts play an important role in shaping the types of institutions that groups use to manage resources, and the effectiveness of those institutions in achieving social and environmental objectives. [...]
2024 - 10.5334/ijc.1226
International Journal of the Commons, Vol. 18, issue 1 (2024) , p. 1-16
|
|
|
5.
|
|
|
Local interpretation of machine learning models in remote sensing with SHAP : the case of global climate constraints on photosynthesis phenology
/
Descals, Adrià (Centre de Recerca Ecològica i d'Aplicacions Forestals) ;
Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals) ;
Yin, Gaofei (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering) ;
Filella, Iolanda (Centre de Recerca Ecològica i d'Aplicacions Forestals) ;
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Data-driven models using machine learning have been widely used in remote-sensing applications such as the retrieval of biophysical variables and land cover classification. However, these models behave as a 'black box', meaning that the relationships between the input and predicted variables are hard to interpret. [...]
2023 - 10.1080/01431161.2023.2217982
International Journal of Remote Sensing, Vol. 44, issue 10 (2023) , p. 3160-3173
|
|
|
6.
|
|
55 p, 707.1 KB |
Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology
/
Pitarch, Carla (Universitat Politècnica de Catalunya. Departament d'Informàtica) ;
Ungan, Gulnur Semahat (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular) ;
Julià Sapé, Ma. Margarita (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí") ;
Vellido, Alfredo (Universitat Politècnica de Catalunya. Departament d'Informàtica)
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. [...]
2024 - 10.3390/cancers16020300
Cancers, Vol. 16, Issue 2 (January 2024) , art. 300
|
|
|
7.
|
|
6 p, 731.5 KB |
A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood : Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort
/
Stamate, Daniel (University of London) ;
Kim, Min (Steno Diabetes Center Copenhagen) ;
Proitsi, Petroula (King's College London) ;
Westwood, Sarah (University of Oxford) ;
Baird, Alison (University of Oxford) ;
Nevado-Holgado, Alejo (University of Oxford) ;
Hye, Abdul (King's College London) ;
Bos, Isabelle (Amsterdam UMC) ;
Vos, Stephanie J.B. (Maastricht University) ;
Vandenberghe, Rik (Amsterdam UMC) ;
Teunissen, Charlotte E. (Amsterdam UMC. University Medical Center) ;
Kate, Mara Ten (Amsterdam UMC. University Medical Center) ;
Scheltens, Philip (Vrije Universiteit Amsterdam) ;
Gabel, Silvy (Laboratory for Cognitive Neurology) ;
Meersmans, Karen (Laboratory for Cognitive Neurology) ;
Blin, Olivier (Aix-Marseille Université) ;
Richardson, Jill (GlaxoSmithKline R&D) ;
De Roeck, Ellen (University of Antwerp) ;
Engelborghs, Sebastiaan (Vrije Universiteit Brussel (VUB)) ;
Sleegers, Kristel (Center for Molecular Neurology. VIB) ;
Bordet, Régis (University of Lille) ;
Ramit, Lorena (Hospital Clínic i Provincial de Barcelona) ;
Kettunen, Petronella (Sahlgrenska Academy at University of Gothenburg) ;
Tsolaki, Magda (AHEPA University Hospital (Grècia)) ;
Verhey, Frans (Maastricht University) ;
Alcolea, Daniel (Institut d'Investigació Biomèdica Sant Pau) ;
Lleó, Alberto (Institut d'Investigació Biomèdica Sant Pau) ;
Peyratout, Gwendoline (Lausanne University Hospital) ;
Tainta, Mikel (Fundacion CITA-alzheimer Fundazioa) ;
Johannsen, Peter (Copenhagen University Hospital) ;
Freund-Levi, Yvonne (Karolinska Institutet (Estocolm, Suècia)) ;
Frölich, Lutz (University of Heidelberg) ;
Dobricic, Valerija (University of Lübeck) ;
Frisoni, Giovanni B. (IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli) ;
Molinuevo, José Luis (Universitat Pompeu Fabra) ;
Wallin, Anders (Sahlgrenska Academy at the University of Gothenburg) ;
Popp, Julius (Geneva University Hospitals) ;
Martinez-Lage, Pablo (Fundación CITA-Alzhéimer Fundazioa) ;
Bertram, Lars (University of Oslo) ;
Blennow, Kaj (Sahlgrenska University Hospital (Suècia)) ;
Zetterberg, Henrik (UCL Institute of Neurology (Regne Unit)) ;
Streffer, Johannes (University of Antwerp) ;
Visser, Pieter J. (Amsterdam UMC) ;
Lovestone, Simon (Janssen-Cilag UK Ltd) ;
Legido-Quigley, Cristina (King's College London) ;
Universitat Autònoma de Barcelona
Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers. [...]
2019 - 10.1016/j.trci.2019.11.001
Alzheimer's & dementia, Vol. 5 (2019) , p. 933-938
|
|
|
8.
|
|
9 p, 241.8 KB |
Severe Disease in Patients With Recent-Onset Psoriatic Arthritis. Prediction Model Based on Machine Learning
/
Queiro, R. (Universidad de Oviedo) ;
Seoane-Mato, D. (Sociedad Española de Reumatología) ;
Laiz, Ana (Institut d'Investigació Biomèdica Sant Pau) ;
Galíndez Agirregoikoa, Eva (Hospital Universitario Basurto) ;
Montilla, C. (Hospital Universitario de Salamanca) ;
Park, Hye-Sang (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya)) ;
Pinto Tasende, J.A. (Complejo Hospitalario Universitario de A Coruña) ;
Bethencourt Baute, Juan José (Hospital Universitario de Canarias (La Laguna)) ;
Joven Ibáñez, B. (Hospital Universitario) ;
Toniolo, E. (Hospital Universitari Son Llàtzer (Palma de Mallorca, Balears)) ;
Ramírez, Julio (Hospital Clínic i Provincial de Barcelona) ;
Pruenza García-Hinojosa, C. (Universidad Autónoma de Madrid) ;
Universitat Autònoma de Barcelona
To identify patient- and disease-related characteristics that make it possible to predict higher disease severity in recent-onset PsA. We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). [...]
2022 - 10.3389/fmed.2022.891863
Frontiers in Medicine, Vol. 9 (28 2022) , p. 891863
|
|
|
9.
|
|
9 p, 1.1 MB |
Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis : predictive model based on machine learning
/
Queiro, R. (Universidad de Oviedo) ;
Seoane-Mato, D. (Sociedad Española de Reumatología) ;
Laiz, Ana (Institut d'Investigació Biomèdica Sant Pau) ;
Agirregoikoa, E.G. (Hospital Universitario Basurto) ;
Montilla, C. (Hospital Universitario de Salamanca) ;
Park, Hye-Sang (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya)) ;
Pinto-Tasende, J.A. (Complejo Hospitalario Universitario de A Coruña) ;
Bethencourt Baute, Juan José (Hospital Universitario de Canarias (La Laguna) ;
Ibáñez, B.J. (Hospital Universitario 12 de Octubre) ;
Toniolo, E. (Hospital Universitari Son Llàtzer (Palma de Mallorca, Balears)) ;
Ramírez, Julio (Hospital Clínic i Provincial de Barcelona) ;
García, A.S. (Universidad Autónoma de Madrid) ;
Cañete, J. D. ;
Juanola, X. ;
Fiter, J. ;
Gratacós, J. ;
Rodriguez-Moreno, J. ;
Rosa, J.N. ;
Martín, A.L. ;
García, A.B. ;
Segura, P.C. ;
López-Ferrer, Anna (Institut d'Investigació Biomèdica Sant Pau) ;
Barrio, S.P. ;
Plata Izquierdo, A.J. ;
Bustabad, S. ;
Guimerá Martín-Neda, F.J. ;
Capdevilla, E.F. ;
Díaz, R.R. ;
Cuervo, A. ;
Gibert, M.A. ;
Larraz, P.T. ;
de la Morena Barrio, I. ;
Lanza, L.P. ;
Sanchís, D.B. ;
Mesquida, C.M. ;
Murillo, C. ;
Moreno Ramos, M.J. ;
Beteta, M.D. ;
Guillén, P.S.P. ;
Oliveira, L.L. ;
Marco, T.N. ;
Cebrián, L. ;
De la Cueva, Pablo ;
Steiner, M. ;
Muñoz-Fernández, S. ;
Garrido, R.V. ;
León, M. ;
Rubio, E. ;
Jiménez, A.M. ;
Fernández-Freire, L. R. ;
Luezas, J.M. ;
Sánchez-González, M.D. ;
Muñoz, C.S. ;
Senabre, J.M. ;
Rosas, J.C. ;
Soler, G.S. ;
Mataix Díaz, F.J. ;
Nieto-González, J.C. ;
González, C. ;
Ovalles Bonilla, J.G. ;
Rodríguez, O.B. ;
Medina, F.J.N. ;
Luján, D. ;
Ruiz Montesino, M.D. ;
Carrizosa Esquivel, A.M. ;
Fernández-Carballido, C. ;
Martínez-Vidal, M.P. ;
Fernández, L.G. ;
Jovani, V. ;
Alameda, R.C. ;
Sabater, S.G. ;
Romero, I.B. ;
Urruticoechea-Arana, A. ;
Torres, M.S. ;
Almodóvar, Raquel ;
López Estebaranz, J.L. ;
López Montilla, M.D. ;
García-Nieto, A.V. ;
Univetrsitat Autònoma de Barcelona
Very few data are available on predictors of minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis (PsA). Such data are crucial, since the therapeutic measures used to change the adverse course of PsA are more likely to succeed if we intervene early. [...]
2022 - 10.1186/s13075-022-02838-2
Arthritis research & therapy, Vol. 24 Núm. 1 (december 2022) , p. 153
|
|
|
10.
|
|
6 p, 420.6 KB |
Characteristics associated with the perception of high-impact disease (PsAID ≥4) in patients with recent-onset psoriatic arthritis. Machine learning-based model
/
Queiro, Ruben (Universidad de Oviedo) ;
Seoane-Mato, Daniel (Sociedad Española de Reumatologia) ;
Laiz, Ana (Institut d'Investigació Biomèdica Sant Pau) ;
Agirregoikoa, Eva Galíndez (Hospital Universitario Basurto) ;
Montilla, Carlos (Hospital Universitario de Salamanca) ;
Park, Hye-Sang (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya)) ;
Pinto-Tasende, Jose A. (Complejo Hospitalario Universitario de A Coruña) ;
Bethencourt Baute, Juan José (Hospital Universitario de Canarias (La Laguna)) ;
Ibáñez, Beatriz Joven (Hospital Universitario 12 de Octubre) ;
Toniolo, Elide (Hospital Universitari Son Llàtzer (Palma de Mallorca, Balears)) ;
Ramírez, Julio (Hospital Clínic i Provincial de Barcelona) ;
García, Ana Serrano (Universidad Autónoma de Madrid) ;
Cañete, Juan D. (Hospital Clínic i Provincial de Barcelona) ;
Juanola, Xavier (Hospital Universitari de Bellvitge) ;
Gratacós, Jordi (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT)) ;
Rodriguez-Moreno, Jesús (Hospital Universitari de Bellvitge) ;
Notario, Jaime (Hospital Universitari de Bellvitge) ;
López-Ferrer, Anna (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya)) ;
Cuervo, Andrea (Hospital Clínic i Provincial de Barcelona) ;
Alsina Gibert, Mercè (Hospital Clínic i Provincial de Barcelona) ;
Universitat Autònoma de Barcelona
To evaluate which patient and disease characteristics are associated with the perception of high-impact disease (PsAID ≥4) in recent-onset psoriatic arthritis. We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). [...]
2022 - 10.1016/j.semarthrit.2022.152097
Seminars in Arthritis and Rheumatism, Vol. 57 (december 2022) , p. 152097
|
|
guest ::
email alert
or subscribe to the 
RSS feed.
