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14 results on '"Bergamin, R."'

1. Heat kernels on cone of $AdS_2$ and $k$-wound circular Wilson loop in $AdS_5 \times S^5$ superstring

Author

Bergamin, R. and Tseytlin, A. A.

Subjects
High Energy Physics - Theory
Abstract

We compute the one-loop world-sheet correction to partition function of $AdS_5 \times S^5$ superstring that should be representing $k$-fundamental circular Wilson loop in planar limit. The 2d metric of the minimal surface ending on $k$-wound circle at the boundary is that of a cone of $AdS_2$ with deficit $2\pi (1-k)$. We compute determinants of 2d fluctuation operators by first constructing heat kernels of scalar and spinor Laplacians on the cone using the Sommerfeld formula. The final expression for the k-dependent part of the one-loop correction has simple integral representation but is different from earlier results., Comment: 16 pages

Published
2015

2. A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

Author

Patruno, L, Milite, S, Bergamin, R, Calonaci, N, D'Onofrio, A, Anselmi, F, Antoniotti, M, Graudenzi, A, Caravagna, G, Patruno L., Milite S., Bergamin R., Calonaci N., D'Onofrio A., Anselmi F., Antoniotti M., Graudenzi A., Caravagna G., Patruno, L, Milite, S, Bergamin, R, Calonaci, N, D'Onofrio, A, Anselmi, F, Antoniotti, M, Graudenzi, A, Caravagna, G, Patruno L., Milite S., Bergamin R., Calonaci N., D'Onofrio A., Anselmi F., Antoniotti M., Graudenzi A., and Caravagna G.

Abstract

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multiomics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.

Published
2023

6. TRY plant trait database enhanced coverage and open access

Author

Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I.C., Leadley, P., Tautenhahn, S., Werner, G.D.A., Aakala, T., Abedi, M., Acosta, A.T.R., Adamidis, G.C., Adamson, K., Aiba, M., Albert, C.H., Alcántara, J.M., Alcázar, C, C., Aleixo, I., Ali, H., Amiaud, B., Ammer, C., Amoroso, M.M., Anand, M., Anderson, C., Anten, N., Antos, J., Apgaua, D.M.G., Ashman, T.-L., Asmara, D.H., Asner, G.P., Aspinwall, M., Atkin, O., Aubin, I., Baastrup-Spohr, L., Bahalkeh, K., Bahn, M., Baker, T., Baker, W.J., Bakker, J.P., Baldocchi, D., Baltzer, J., Banerjee, A., Baranger, A., Barlow, J., Barneche, D.R., Baruch, Z., Bastianelli, D., Battles, J., Bauerle, W., Bauters, M., Bazzato, E., Beckmann, M., Beeckman, H., Beierkuhnlein, C., Bekker, R., Belfry, G., Belluau, M., Beloiu, M., Benavides, R., Benomar, L., Berdugo-Lattke, M.L., Berenguer, E., Bergamin, R., Bergmann, J., Bergmann, Carlucci, M., Berner, L., Bernhardt-Römermann, M., Bigler, C., Bjorkman, A.D., Blackman, C., Blanco, C., Blonder, B., Blumenthal, D., Bocanegra-González, K.T., Boeckx, P., Bohlman, S., Böhning-Gaese, K., Boisvert-Marsh, L., Bond, W., Bond-Lamberty, B., Boom, A., Boonman, C.C.F., Bordin, K., Boughton, E.H., Boukili, V., Bowman, D.M.J.S., Bravo, S., Brendel, M.R., Broadley, M.R., Brown, K.A., Bruelheide, H., Brumnich, F., Bruun, H.H., Bruy, D., Buchanan, S.W., Bucher, S.F., Buchmann, N., Buitenwerf, R., Bunker, D.E., Bürger, J., Burrascano, Sabina, Burslem, D.F.R.P., Butterfield, B.J., Byun, C., Marques, M., Scalon, M.C., Caccianiga, M., Cadotte, M., Cailleret, M., Camac, J., Camarero, J.J., Campany, C., Campetella, G., Campos Prieto, Juan Antonio, Cano-Arboleda, L., Canullo, R., Carbognani, M., Carvalho, F., Casanoves, F., Castagneyrol, B., Catford, J.A., Cavender-Bares, J., Cerabolini, Bruno E. L., Cervellini, M., Chacón-Madrigal, E., Chapin, K., Chapin, F.S., Chelli, S., Chen, S.-C., Chen, A., Cherubini, P., Chianucci, F., Choat, B., Chung, K.-S., Chytrý, Milan, Ciccarelli, D., Coll, L., Collins, C.G., Conti, L., Coomes, D., Cornelissen, J.H.C., Cornwell, W.K., Corona, P., Coyea, M., Craine, J., Craven, D., Cromsigt, J.P.G.M., Csecserits, A., Cufar, K., Cuntz, M., and da, Silva, A.C

Abstract

Plant traits the morphological, anatomical, physiological, biochemical and phenological characteristics of plants determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits almost complete coverage for plant growth form . However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives. © 2019 The Authors. Global Change Biology published by John Wiley and Sons Ltd

Published
2020

7. TRY plant trait database – enhanced coverage and open access

Author

Kattge, J, Bönisch, G, Díaz, S, Lavorel, S, Prentice, IC, Leadley, P, Tautenhahn, S, Werner, GDA, Aakala, T, Abedi, M, Acosta, ATR, Adamidis, GC, Adamson, K, Aiba, M, Albert, CH, Alcántara, JM, Alcázar C, C, Aleixo, I, Ali, H, Amiaud, B, Ammer, C, Amoroso, MM, Anand, M, Anderson, C, Anten, N, Antos, J, Apgaua, DMG, Ashman, TL, Asmara, DH, Asner, GP, Aspinwall, M, Atkin, O, Aubin, I, Baastrup-Spohr, L, Bahalkeh, K, Bahn, M, Baker, T, Baker, WJ, Bakker, JP, Baldocchi, D, Baltzer, J, Banerjee, A, Baranger, A, Barlow, J, Barneche, DR, Baruch, Z, Bastianelli, D, Battles, J, Bauerle, W, Bauters, M, Bazzato, E, Beckmann, M, Beeckman, H, Beierkuhnlein, C, Bekker, R, Belfry, G, Belluau, M, Beloiu, M, Benavides, R, Benomar, L, Berdugo-Lattke, ML, Berenguer, E, Bergamin, R, Bergmann, J, Bergmann Carlucci, M, Berner, L, Bernhardt-Römermann, M, Bigler, C, Bjorkman, AD, Blackman, C, Blanco, C, Blonder, B, Blumenthal, D, Bocanegra-González, KT, Boeckx, P, Bohlman, S, Böhning-Gaese, K, Boisvert-Marsh, L, Bond, W, Bond-Lamberty, B, Boom, A, Boonman, CCF, Bordin, K, Boughton, EH, Boukili, V, Bowman, DMJS, Bravo, S, Brendel, MR, Broadley, MR, Brown, KA, Bruelheide, H, Brumnich, F, Bruun, HH, Bruy, D, Buchanan, SW, Bucher, SF, Buchmann, N, Buitenwerf, R, Bunker, DE, Bürger, J, Kattge, J, Bönisch, G, Díaz, S, Lavorel, S, Prentice, IC, Leadley, P, Tautenhahn, S, Werner, GDA, Aakala, T, Abedi, M, Acosta, ATR, Adamidis, GC, Adamson, K, Aiba, M, Albert, CH, Alcántara, JM, Alcázar C, C, Aleixo, I, Ali, H, Amiaud, B, Ammer, C, Amoroso, MM, Anand, M, Anderson, C, Anten, N, Antos, J, Apgaua, DMG, Ashman, TL, Asmara, DH, Asner, GP, Aspinwall, M, Atkin, O, Aubin, I, Baastrup-Spohr, L, Bahalkeh, K, Bahn, M, Baker, T, Baker, WJ, Bakker, JP, Baldocchi, D, Baltzer, J, Banerjee, A, Baranger, A, Barlow, J, Barneche, DR, Baruch, Z, Bastianelli, D, Battles, J, Bauerle, W, Bauters, M, Bazzato, E, Beckmann, M, Beeckman, H, Beierkuhnlein, C, Bekker, R, Belfry, G, Belluau, M, Beloiu, M, Benavides, R, Benomar, L, Berdugo-Lattke, ML, Berenguer, E, Bergamin, R, Bergmann, J, Bergmann Carlucci, M, Berner, L, Bernhardt-Römermann, M, Bigler, C, Bjorkman, AD, Blackman, C, Blanco, C, Blonder, B, Blumenthal, D, Bocanegra-González, KT, Boeckx, P, Bohlman, S, Böhning-Gaese, K, Boisvert-Marsh, L, Bond, W, Bond-Lamberty, B, Boom, A, Boonman, CCF, Bordin, K, Boughton, EH, Boukili, V, Bowman, DMJS, Bravo, S, Brendel, MR, Broadley, MR, Brown, KA, Bruelheide, H, Brumnich, F, Bruun, HH, Bruy, D, Buchanan, SW, Bucher, SF, Buchmann, N, Buitenwerf, R, Bunker, DE, and Bürger, J

Abstract

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Published
2020

9. Energiecontracten

Author

Loos, M.B.M., Stout, H., Bergamin, R., and Overig onderzoek Privaatrecht

Published
2001

10. Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc.

Author

Antonello A, Bergamin R, Calonaci N, Househam J, Milite S, Williams MJ, Anselmi F, d'Onofrio A, Sundaram V, Sosinsky A, Cross WCH, and Caravagna G

Subjects
Humans, Algorithms, Polymorphism, Single Nucleotide, Genomics methods, Computational Biology methods, DNA Copy Number Variations, Neoplasms genetics, Neoplasms pathology
Abstract

Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform the computational validation of clonal and subclonal CNAs detected from bulk DNA sequencing. CNAqc is validated using single-cell data and simulations, is applied to over 4000 TCGA and PCAWG samples, and is incorporated into the validation process for the clinically accredited bioinformatics pipeline at Genomics England. CNAqc is designed to support automated quality control procedures for tumor somatic data validation., (© 2024. The Author(s).)

Published
2024
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11. A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing.

Author

Patruno L, Milite S, Bergamin R, Calonaci N, D'Onofrio A, Anselmi F, Antoniotti M, Graudenzi A, and Caravagna G

Subjects
Bayes Theorem, Clone Cells, High-Throughput Nucleotide Sequencing methods, Chromatin, RNA genetics, DNA Copy Number Variations genetics
Abstract

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Patruno et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2023
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12. A Bayesian method to cluster single-cell RNA sequencing data using copy number alterations.

Author

Milite S, Bergamin R, Patruno L, Calonaci N, and Caravagna G

Subjects
Humans, Bayes Theorem, Software, Sequence Analysis, RNA, RNA, Single-Cell Analysis, DNA Copy Number Variations, Neoplasms genetics
Abstract

Motivation: Cancers are composed by several heterogeneous subpopulations, each one harbouring different genetic and epigenetic somatic alterations that contribute to disease onset and therapy response. In recent years, copy number alterations (CNAs) leading to tumour aneuploidy have been identified as potential key drivers of such populations, but the definition of the precise makeup of cancer subclones from sequencing assays remains challenging. In the end, little is known about the mapping between complex CNAs and their effect on cancer phenotypes., Results: We introduce CONGAS, a Bayesian probabilistic method to phase bulk DNA and single-cell RNA measurements from independent assays. CONGAS jointly identifies clusters of single cells with subclonal CNAs, and differences in RNA expression. The model builds statistical priors leveraging bulk DNA sequencing data, does not require a normal reference and scales fast thanks to a GPU backend and variational inference. We test CONGAS on both simulated and real data, and find that it can determine the tumour subclonal composition at the single-cell level together with clone-specific RNA phenotypes in tumour data generated from both 10× and Smart-Seq assays., Availability and Implementation: CONGAS is available as 2 packages: CONGAS (https://github.com/caravagnalab/congas), which implements the model in Python, and RCONGAS (https://caravagnalab.github.io/rcongas/), which provides R functions to process inputs, outputs and run CONGAS fits. The analysis of real data and scripts to generate figures of this paper are available via RCONGAS; code associated to simulations is available at https://github.com/caravagnalab/rcongas_test., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Published
2022
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13. TRY plant trait database - enhanced coverage and open access.

Author

Kattge J, Bönisch G, Díaz S, Lavorel S, Prentice IC, Leadley P, Tautenhahn S, Werner GDA, Aakala T, Abedi M, Acosta ATR, Adamidis GC, Adamson K, Aiba M, Albert CH, Alcántara JM, Alcázar C C, Aleixo I, Ali H, Amiaud B, Ammer C, Amoroso MM, Anand M, Anderson C, Anten N, Antos J, Apgaua DMG, Ashman TL, Asmara DH, Asner GP, Aspinwall M, Atkin O, Aubin I, Baastrup-Spohr L, Bahalkeh K, Bahn M, Baker T, Baker WJ, Bakker JP, Baldocchi D, Baltzer J, Banerjee A, Baranger A, Barlow J, Barneche DR, Baruch Z, Bastianelli D, Battles J, Bauerle W, Bauters M, Bazzato E, Beckmann M, Beeckman H, Beierkuhnlein C, Bekker R, Belfry G, Belluau M, Beloiu M, Benavides R, Benomar L, Berdugo-Lattke ML, Berenguer E, Bergamin R, Bergmann J, Bergmann Carlucci M, Berner L, Bernhardt-Römermann M, Bigler C, Bjorkman AD, Blackman C, Blanco C, Blonder B, Blumenthal D, Bocanegra-González KT, Boeckx P, Bohlman S, Böhning-Gaese K, Boisvert-Marsh L, Bond W, Bond-Lamberty B, Boom A, Boonman CCF, Bordin K, Boughton EH, Boukili V, Bowman DMJS, Bravo S, Brendel MR, Broadley MR, Brown KA, Bruelheide H, Brumnich F, Bruun HH, Bruy D, Buchanan SW, Bucher SF, Buchmann N, Buitenwerf R, Bunker DE, Bürger J, Burrascano S, Burslem DFRP, Butterfield BJ, Byun C, Marques M, Scalon MC, Caccianiga M, Cadotte M, Cailleret M, Camac J, Camarero JJ, Campany C, Campetella G, Campos JA, Cano-Arboleda L, Canullo R, Carbognani M, Carvalho F, Casanoves F, Castagneyrol B, Catford JA, Cavender-Bares J, Cerabolini BEL, Cervellini M, Chacón-Madrigal E, Chapin K, Chapin FS, Chelli S, Chen SC, Chen A, Cherubini P, Chianucci F, Choat B, Chung KS, Chytrý M, Ciccarelli D, Coll L, Collins CG, Conti L, Coomes D, Cornelissen JHC, Cornwell WK, Corona P, Coyea M, Craine J, Craven D, Cromsigt JPGM, Csecserits A, Cufar K, Cuntz M, da Silva AC, Dahlin KM, Dainese M, Dalke I, Dalle Fratte M, Dang-Le AT, Danihelka J, Dannoura M, Dawson S, de Beer AJ, De Frutos A, De Long JR, Dechant B, Delagrange S, Delpierre N, Derroire G, Dias AS, Diaz-Toribio MH, Dimitrakopoulos PG, Dobrowolski M, Doktor D, Dřevojan P, Dong N, Dransfield J, Dressler S, Duarte L, Ducouret E, Dullinger S, Durka W, Duursma R, Dymova O, E-Vojtkó A, Eckstein RL, Ejtehadi H, Elser J, Emilio T, Engemann K, Erfanian MB, Erfmeier A, Esquivel-Muelbert A, Esser G, Estiarte M, Domingues TF, Fagan WF, Fagúndez J, Falster DS, Fan Y, Fang J, Farris E, Fazlioglu F, Feng Y, Fernandez-Mendez F, Ferrara C, Ferreira J, Fidelis A, Finegan B, Firn J, Flowers TJ, Flynn DFB, Fontana V, Forey E, Forgiarini C, François L, Frangipani M, Frank D, Frenette-Dussault C, Freschet GT, Fry EL, Fyllas NM, Mazzochini GG, Gachet S, Gallagher R, Ganade G, Ganga F, García-Palacios P, Gargaglione V, Garnier E, Garrido JL, de Gasper AL, Gea-Izquierdo G, Gibson D, Gillison AN, Giroldo A, Glasenhardt MC, Gleason S, Gliesch M, Goldberg E, Göldel B, Gonzalez-Akre E, Gonzalez-Andujar JL, González-Melo A, González-Robles A, Graae BJ, Granda E, Graves S, Green WA, Gregor T, Gross N, Guerin GR, Günther A, Gutiérrez AG, Haddock L, Haines A, Hall J, Hambuckers A, Han W, Harrison SP, Hattingh W, Hawes JE, He T, He P, Heberling JM, Helm A, Hempel S, Hentschel J, Hérault B, Hereş AM, Herz K, Heuertz M, Hickler T, Hietz P, Higuchi P, Hipp AL, Hirons A, Hock M, Hogan JA, Holl K, Honnay O, Hornstein D, Hou E, Hough-Snee N, Hovstad KA, Ichie T, Igić B, Illa E, Isaac M, Ishihara M, Ivanov L, Ivanova L, Iversen CM, Izquierdo J, Jackson RB, Jackson B, Jactel H, Jagodzinski AM, Jandt U, Jansen S, Jenkins T, Jentsch A, Jespersen JRP, Jiang GF, Johansen JL, Johnson D, Jokela EJ, Joly CA, Jordan GJ, Joseph GS, Junaedi D, Junker RR, Justes E, Kabzems R, Kane J, Kaplan Z, Kattenborn T, Kavelenova L, Kearsley E, Kempel A, Kenzo T, Kerkhoff A, Khalil MI, Kinlock NL, Kissling WD, Kitajima K, Kitzberger T, Kjøller R, Klein T, Kleyer M, Klimešová J, Klipel J, Kloeppel B, Klotz S, Knops JMH, Kohyama T, Koike F, Kollmann J, Komac B, Komatsu K, König C, Kraft NJB, Kramer K, Kreft H, Kühn I, Kumarathunge D, Kuppler J, Kurokawa H, Kurosawa Y, Kuyah S, Laclau JP, Lafleur B, Lallai E, Lamb E, Lamprecht A, Larkin DJ, Laughlin D, Le Bagousse-Pinguet Y, le Maire G, le Roux PC, le Roux E, Lee T, Lens F, Lewis SL, Lhotsky B, Li Y, Li X, Lichstein JW, Liebergesell M, Lim JY, Lin YS, Linares JC, Liu C, Liu D, Liu U, Livingstone S, Llusià J, Lohbeck M, López-García Á, Lopez-Gonzalez G, Lososová Z, Louault F, Lukács BA, Lukeš P, Luo Y, Lussu M, Ma S, Maciel Rabelo Pereira C, Mack M, Maire V, Mäkelä A, Mäkinen H, Malhado ACM, Mallik A, Manning P, Manzoni S, Marchetti Z, Marchino L, Marcilio-Silva V, Marcon E, Marignani M, Markesteijn L, Martin A, Martínez-Garza C, Martínez-Vilalta J, Mašková T, Mason K, Mason N, Massad TJ, Masse J, Mayrose I, McCarthy J, McCormack ML, McCulloh K, McFadden IR, McGill BJ, McPartland MY, Medeiros JS, Medlyn B, Meerts P, Mehrabi Z, Meir P, Melo FPL, Mencuccini M, Meredieu C, Messier J, Mészáros I, Metsaranta J, Michaletz ST, Michelaki C, Migalina S, Milla R, Miller JED, Minden V, Ming R, Mokany K, Moles AT, Molnár A 5th, Molofsky J, Molz M, Montgomery RA, Monty A, Moravcová L, Moreno-Martínez A, Moretti M, Mori AS, Mori S, Morris D, Morrison J, Mucina L, Mueller S, Muir CD, Müller SC, Munoz F, Myers-Smith IH, Myster RW, Nagano M, Naidu S, Narayanan A, Natesan B, Negoita L, Nelson AS, Neuschulz EL, Ni J, Niedrist G, Nieto J, Niinemets Ü, Nolan R, Nottebrock H, Nouvellon Y, Novakovskiy A, Nystuen KO, O'Grady A, O'Hara K, O'Reilly-Nugent A, Oakley S, Oberhuber W, Ohtsuka T, Oliveira R, Öllerer K, Olson ME, Onipchenko V, Onoda Y, Onstein RE, Ordonez JC, Osada N, Ostonen I, Ottaviani G, Otto S, Overbeck GE, Ozinga WA, Pahl AT, Paine CET, Pakeman RJ, Papageorgiou AC, Parfionova E, Pärtel M, Patacca M, Paula S, Paule J, Pauli H, Pausas JG, Peco B, Penuelas J, Perea A, Peri PL, Petisco-Souza AC, Petraglia A, Petritan AM, Phillips OL, Pierce S, Pillar VD, Pisek J, Pomogaybin A, Poorter H, Portsmuth A, Poschlod P, Potvin C, Pounds D, Powell AS, Power SA, Prinzing A, Puglielli G, Pyšek P, Raevel V, Rammig A, Ransijn J, Ray CA, Reich PB, Reichstein M, Reid DEB, Réjou-Méchain M, de Dios VR, Ribeiro S, Richardson S, Riibak K, Rillig MC, Riviera F, Robert EMR, Roberts S, Robroek B, Roddy A, Rodrigues AV, Rogers A, Rollinson E, Rolo V, Römermann C, Ronzhina D, Roscher C, Rosell JA, Rosenfield MF, Rossi C, Roy DB, Royer-Tardif S, Rüger N, Ruiz-Peinado R, Rumpf SB, Rusch GM, Ryo M, Sack L, Saldaña A, Salgado-Negret B, Salguero-Gomez R, Santa-Regina I, Santacruz-García AC, Santos J, Sardans J, Schamp B, Scherer-Lorenzen M, Schleuning M, Schmid B, Schmidt M, Schmitt S, Schneider JV, Schowanek SD, Schrader J, Schrodt F, Schuldt B, Schurr F, Selaya Garvizu G, Semchenko M, Seymour C, Sfair JC, Sharpe JM, Sheppard CS, Sheremetiev S, Shiodera S, Shipley B, Shovon TA, Siebenkäs A, Sierra C, Silva V, Silva M, Sitzia T, Sjöman H, Slot M, Smith NG, Sodhi D, Soltis P, Soltis D, Somers B, Sonnier G, Sørensen MV, Sosinski EE Jr, Soudzilovskaia NA, 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Subjects
Biodiversity, Ecology, Plants, Access to Information, Ecosystem
Abstract

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives., (© 2019 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Published
2020
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14. The role of fluorine-18-deoxyglucose (FDG) positron emission tomography (PET) whole body scan (WBS) in the staging and follow-up of cancer patients: our first experience.

Author

Ferlin G, Rubello D, Chierichetti F, Zanco P, Bergamin R, Trento P, Fini A, and Cargnel S

Subjects
Female, Fluorodeoxyglucose F18, Follow-Up Studies, Humans, Male, Middle Aged, Neoplasm Staging, Predictive Value of Tests, Technetium Tc 99m Medronate, Tomography, X-Ray Computed, Ultrasonography, Deoxyglucose analogs & derivatives, Fluorine Radioisotopes, Neoplasms diagnostic imaging, Neoplasms pathology, Radiopharmaceuticals, Tomography, Emission-Computed methods
Abstract

We report the results of FDG PET whole body scan in 75 cancer patients in whom tumor extent was defined by surgical, histological or cytological findings and clinical follow-up. Twenty-five had malignant lymphomas, 24 lung carcinomas, and 26 other types of solid tumors. Twenty-three patients were evaluated at disease onset, before therapy, and 37 at the moment of tumor recurrence; the remaining 15 patients were in complete remission after treatment and were taken as controls. Visual and quantitative PET results were compared with conventional imaging (US, CT scan and/or MRI, and Tc99m MDP bone scan). In the 60 patients with active disease, PET as well as conventional imaging were able to locate the primary tumor in all 23 patients studied at disease onset. However, with regard to lymph node and distant metastases, PET provided the same information as conventional imaging in 31 cases (51.6%), but revealed further neoplastic foci in 29 cases (48.4%), 21 in lymph nodes and 8 at distant sites. The sensitivity of PET, in comparison with conventional imaging, was 100% versus 100% for the detection of the primary tumor, 97.6% versus 55.8% for the localization of node metastases, and 100% versus 55.5% for the visualization of distant metastases. The specificity, calculated in the group of 15 disease-free patients, was 100% for PET and 86.6% for conventional imaging. The therapeutic approach was modified in 12 patients (20%) on the basis of the PET results. Furthermore, in 14 cases (23.3%) with advanced disease, PET provided complete information on tumor spread, otherwise obtainable only by taking together the results of all other diagnostic procedures. Our data indicate a higher accuracy of FDG PET whole body scan compared to conventional imaging techniques in the evaluation of metastatic spread both at initial diagnosis and during follow-up, with an important impact on therapeutic decision-making. Moreover, by providing complete information on tumor spread in some cases, PET can become a profitable tool in terms of cost reduction.

Published
1997
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