TY  - JOUR
A1  - Sathyanarayana, Vijaya
A1  - Lee, Beth
A1  - Wright, Neville B.
A1  - Santos, Rui
A1  - Bonney, Denise
A1  - Wynn, Robert
A1  - Patel, Leena
A1  - Chandler, Kate
A1  - Cheesman, Ed
A1  - Schindler, Detlev
A1  - Webb, Nicholas J. A.
A1  - Meyer, Stefan
T1  - Patterns and frequency of renal abnormalities in Fanconi anaemia: implications for long-term management
JF  - Pediatric Nephrology
N2  - Fanconi anaemia (FA) is an inherited disease with bone marrow failure, variable congenital and developmental abnormalities, and cancer predisposition. With improved survival, non-haematological manifestations of FA become increasingly important for long-term management. While renal abnormalities are recognized, detailed data on patterns and frequency and implications for long-term management are sparse. We reviewed clinical course and imaging findings of FA patients with respect to renal complications in our centre over a 25-year period to formulate some practical suggestions for guidelines for management of renal problems associated with FA. Thirty patients including four sibling sets were reviewed. On imaging, 14 had evidence of anatomical abnormalities of the kidneys. Two cases with severe phenotype, including renal abnormalities, had chronic kidney disease (CKD) at diagnosis. Haematopoietic stem cell transplantation was complicated by significant acute kidney injury (AKI) in three cases. In three patients, there was CKD at long-term follow-up. All patients had normal blood pressure. Evaluation of renal anatomy with ultrasound imaging is important at diagnostic workup of FA. While CKD is uncommon at diagnosis, our data suggests that the incidence of CKD increases with age, in particular after haematopoietic stem cell transplantation. Monitoring of renal function is essential for management of FA. Based on these long-term clinical observations, we formulate some practical guidelines for assessment and management of renal abnormalities in FA.
KW  - Fanconi anaemia
KW  - Renal abnormalities
KW  - Long-term follow-up
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-227400
VL  - 33
IS  - 9
ER  - 
TY  - JOUR
A1  - Evdokimov, Dimitar
A1  - Frank, Johanna
A1  - Klitsch, Alexander
A1  - Unterecker, Stefan
A1  - Warrings, Bodo
A1  - Serra, Jordi
A1  - Papagianni, Aikaterini
A1  - Saffer, Nadine
A1  - Meyer zu Altenschildesche, Caren
A1  - Kampik, Daniel
A1  - Malik, Rayaz A.
A1  - Sommer, Claudia
A1  - Üceyler, Nurcan
T1  - Reduction of skin innervation is associated with a severe fibromyalgia phenotype
JF  - Annals of Neurology
N2  - Objective: To assess patterns and impact of small nerve fiber dysfunction and pathology in patients with fibromyalgia syndrome (FMS).

Methods: One hundred seventeen women with FMS underwent neurological examination, questionnaire assessment, neurophysiology assessment, and small fiber tests: skin punch biopsy, corneal confocal microscopy, microneurography, quantitative sensory testing including C-tactile afferents, and pain-related evoked potentials. Data were compared with those of women with major depressive disorder and chronic widespread pain (MD-P) and healthy women.

Results: Intraepidermal nerve fiber density (IENFD) was reduced at different biopsy sites in 63% of FMS patients (MDP: 10%, controls: 18%; p < 0.001 for each). We found 4 patterns of skin innervation in FMS: normal, distally reduced, proximally reduced, and both distally and proximally reduced (p < 0.01 for each compared to controls). Microneurography revealed initial activity-dependent acceleration of conduction velocity upon low frequencies of stimulation in 1A fibers, besides 1B fiber spontaneous activity and mechanical sensitization in FMS patients. FMS patients had elevated warm detection thresholds (p < 0.01), impaired C-tactile afferents (p < 0.05), and reduced amplitudes (p < 0.001) of pain-related evoked potentials compared to controls. Compared to FMS patients with normal skin innervation, those with generalized IENFD reduction had higher pain intensity and impairment due to pain, higher disease burden, more stabbing pain and paresthesias, and more anxiety (p < 0.05 for each). FMS patients with generalized IENFD reduction also had lower corneal nerve fiber density (p < 0.01) and length (p < 0.05).

Interpretation: The extent of small fiber pathology is related to symptom severity in FMS. This knowledge may have implications for the diagnostic classification and treatment of patients with FMS.
KW  - fibromyalgia
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-206168
VL  - 86
IS  - 4
ER  - 
TY  - JOUR
A1  - Ziegler, Alice
A1  - Meyer, Hanna
A1  - Otte, Insa
A1  - Peters, Marcell K.
A1  - Appelhans, Tim
A1  - Behler, Christina
A1  - Böhning-Gaese, Katrin
A1  - Classen, Alice
A1  - Detsch, Florian
A1  - Deckert, Jürgen
A1  - Eardley, Connal D.
A1  - Ferger, Stefan W.
A1  - Fischer, Markus
A1  - Gebert, Friederike
A1  - Haas, Michael
A1  - Helbig-Bonitz, Maria
A1  - Hemp, Andreas
A1  - Hemp, Claudia
A1  - Kakengi, Victor
A1  - Mayr, Antonia V.
A1  - Ngereza, Christine
A1  - Reudenbach, Christoph
A1  - Röder, Juliane
A1  - Rutten, Gemma
A1  - Schellenberger Costa, David
A1  - Schleuning, Matthias
A1  - Ssymank, Axel
A1  - Steffan-Dewenter, Ingolf
A1  - Tardanico, Joseph
A1  - Tschapka, Marco
A1  - Vollstädt, Maximilian G. R.
A1  - Wöllauer, Stephan
A1  - Zhang, Jie
A1  - Brandl, Roland
A1  - Nauss, Thomas
T1  - Potential of airborne LiDAR derived vegetation structure for the prediction of animal species richness at Mount Kilimanjaro
JF  - Remote Sensing
N2  - The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.
KW  - biodiversity
KW  - species richness
KW  - LiDAR
KW  - elevation
KW  - partial least square regression
KW  - arthropods
KW  - birds
KW  - bats
KW  - predictive modeling
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262251
SN  - 2072-4292
VL  - 14
IS  - 3
ER  - 
TY  - JOUR
A1  - Dornelas, Maria
A1  - Antão, Laura H.
A1  - Moyes, Faye
A1  - Bates, Amanda E.
A1  - Magurran, Anne E.
A1  - Adam, Dušan
A1  - Akhmetzhanova, Asem A.
A1  - Appeltans, Ward
A1  - Arcos, José Manuel
A1  - Arnold, Haley
A1  - Ayyappan, Narayanan
A1  - Badihi, Gal
A1  - Baird, Andrew H.
A1  - Barbosa, Miguel
A1  - Barreto, Tiago Egydio
A1  - Bässler, Claus
A1  - Bellgrove, Alecia
A1  - Belmaker, Jonathan
A1  - Benedetti-Cecchi, Lisandro
A1  - Bett, Brian J.
A1  - Bjorkman, Anne D.
A1  - Błażewicz, Magdalena
A1  - Blowes, Shane A.
A1  - Bloch, Christopher P. Bloch
A1  - Bonebrake, Timothy C.
A1  - Boyd, Susan
A1  - Bradford, Matt
A1  - Brooks, Andrew J.
A1  - Brown, James H.
A1  - Bruelheide, Helge
A1  - Budy, Phaedra
A1  - Carvalho, Fernando
A1  - Castañeda-Moya, Edward
A1  - Chen, Chaolun Allen
A1  - Chamblee, John F.
A1  - Chase, Tory J.
A1  - Siegwart Collier, Laura
A1  - Collinge, Sharon K.
A1  - Condit, Richard
A1  - Cooper, Elisabeth J.
A1  - Cornelissen, J. Hans C.
A1  - Cotano, Unai
A1  - Crow, Shannan Kyle
A1  - Damasceno, Gabriella
A1  - Davies, Claire H.
A1  - Davis, Robert A.
A1  - Day, Frank P.
A1  - Degraer, Steven
A1  - Doherty, Tim S.
A1  - Dunn, Timothy E.
A1  - Durigan, Giselda
A1  - Duffy, J. Emmett
A1  - Edelist, Dor
A1  - Edgar, Graham J.
A1  - Elahi, Robin
A1  - Elmendorf, Sarah C.
A1  - Enemar, Anders
A1  - Ernest, S. K. Morgan
A1  - Escribano, Rubén
A1  - Estiarte, Marc
A1  - Evans, Brian S.
A1  - Fan, Tung-Yung
A1  - Turini Farah, Fabiano
A1  - Loureiro Fernandes, Luiz
A1  - Farneda, Fábio Z.
A1  - Fidelis, Alessandra
A1  - Fitt, Robert
A1  - Fosaa, Anna Maria
A1  - Franco, Geraldo Antonio Daher Correa
A1  - Frank, Grace E.
A1  - Fraser, William R.
A1  - García, Hernando
A1  - Cazzolla Gatti, Roberto
A1  - Givan, Or
A1  - Gorgone-Barbosa, Elizabeth
A1  - Gould, William A.
A1  - Gries, Corinna
A1  - Grossman, Gary D.
A1  - Gutierréz, Julio R.
A1  - Hale, Stephen
A1  - Harmon, Mark E.
A1  - Harte, John
A1  - Haskins, Gary
A1  - Henshaw, Donald L.
A1  - Hermanutz, Luise
A1  - Hidalgo, Pamela
A1  - Higuchi, Pedro
A1  - Hoey, Andrew
A1  - Van Hoey, Gert
A1  - Hofgaard, Annika
A1  - Holeck, Kristen
A1  - Hollister, Robert D.
A1  - Holmes, Richard
A1  - Hoogenboom, Mia
A1  - Hsieh, Chih-hao
A1  - Hubbell, Stephen P.
A1  - Huettmann, Falk
A1  - Huffard, Christine L.
A1  - Hurlbert, Allen H.
A1  - Ivanauskas, Natália Macedo
A1  - Janík, David
A1  - Jandt, Ute
A1  - Jażdżewska, Anna
A1  - Johannessen, Tore
A1  - Johnstone, Jill
A1  - Jones, Julia
A1  - Jones, Faith A. M.
A1  - Kang, Jungwon
A1  - Kartawijaya, Tasrif
A1  - Keeley, Erin C.
A1  - Kelt, Douglas A.
A1  - Kinnear, Rebecca
A1  - Klanderud, Kari
A1  - Knutsen, Halvor
A1  - Koenig, Christopher C.
A1  - Kortz, Alessandra R.
A1  - Král, Kamil
A1  - Kuhnz, Linda A.
A1  - Kuo, Chao-Yang
A1  - Kushner, David J.
A1  - Laguionie-Marchais, Claire
A1  - Lancaster, Lesley T.
A1  - Lee, Cheol Min
A1  - Lefcheck, Jonathan S.
A1  - Lévesque, Esther
A1  - Lightfoot, David
A1  - Lloret, Francisco
A1  - Lloyd, John D.
A1  - López-Baucells, Adrià
A1  - Louzao, Maite
A1  - Madin, Joshua S.
A1  - Magnússon, Borgþór
A1  - Malamud, Shahar
A1  - Matthews, Iain
A1  - McFarland, Kent P.
A1  - McGill, Brian
A1  - McKnight, Diane
A1  - McLarney, William O.
A1  - Meador, Jason
A1  - Meserve, Peter L.
A1  - Metcalfe, Daniel J.
A1  - Meyer, Christoph F. J.
A1  - Michelsen, Anders
A1  - Milchakova, Nataliya
A1  - Moens, Tom
A1  - Moland, Even
A1  - Moore, Jon
A1  - Moreira, Carolina Mathias
A1  - Müller, Jörg
A1  - Murphy, Grace
A1  - Myers-Smith, Isla H.
A1  - Myster, Randall W.
A1  - Naumov, Andrew
A1  - Neat, Francis
A1  - Nelson, James A.
A1  - Nelson, Michael Paul
A1  - Newton, Stephen F.
A1  - Norden, Natalia
A1  - Oliver, Jeffrey C.
A1  - Olsen, Esben M.
A1  - Onipchenko, Vladimir G.
A1  - Pabis, Krzysztof
A1  - Pabst, Robert J.
A1  - Paquette, Alain
A1  - Pardede, Sinta
A1  - Paterson, David M.
A1  - Pélissier, Raphaël
A1  - Peñuelas, Josep
A1  - Pérez-Matus, Alejandro
A1  - Pizarro, Oscar
A1  - Pomati, Francesco
A1  - Post, Eric
A1  - Prins, Herbert H. T.
A1  - Priscu, John C.
A1  - Provoost, Pieter
A1  - Prudic, Kathleen L.
A1  - Pulliainen, Erkki
A1  - Ramesh, B. R.
A1  - Ramos, Olivia Mendivil
A1  - Rassweiler, Andrew
A1  - Rebelo, Jose Eduardo
A1  - Reed, Daniel C.
A1  - Reich, Peter B.
A1  - Remillard, Suzanne M.
A1  - Richardson, Anthony J.
A1  - Richardson, J. Paul
A1  - van Rijn, Itai
A1  - Rocha, Ricardo
A1  - Rivera-Monroy, Victor H.
A1  - Rixen, Christian
A1  - Robinson, Kevin P.
A1  - Rodrigues, Ricardo Ribeiro
A1  - de Cerqueira Rossa-Feres, Denise
A1  - Rudstam, Lars
A1  - Ruhl, Henry
A1  - Ruz, Catalina S.
A1  - Sampaio, Erica M.
A1  - Rybicki, Nancy
A1  - Rypel, Andrew
A1  - Sal, Sofia
A1  - Salgado, Beatriz
A1  - Santos, Flavio A. M.
A1  - Savassi-Coutinho, Ana Paula
A1  - Scanga, Sara
A1  - Schmidt, Jochen
A1  - Schooley, Robert
A1  - Setiawan, Fakhrizal
A1  - Shao, Kwang-Tsao
A1  - Shaver, Gaius R.
A1  - Sherman, Sally
A1  - Sherry, Thomas W.
A1  - Siciński, Jacek
A1  - Sievers, Caya
A1  - da Silva, Ana Carolina
A1  - da Silva, Fernando Rodrigues
A1  - Silveira, Fabio L.
A1  - Slingsby, Jasper
A1  - Smart, Tracey
A1  - Snell, Sara J.
A1  - Soudzilovskaia, Nadejda A.
A1  - Souza, Gabriel B. G.
A1  - Souza, Flaviana Maluf
A1  - Souza, Vinícius Castro
A1  - Stallings, Christopher D.
A1  - Stanforth, Rowan
A1  - Stanley, Emily H.
A1  - Sterza, José Mauro
A1  - Stevens, Maarten
A1  - Stuart-Smith, Rick
A1  - Suarez, Yzel Rondon
A1  - Supp, Sarah
A1  - Tamashiro, Jorge Yoshio
A1  - Tarigan, Sukmaraharja
A1  - Thiede, Gary P.
A1  - Thorn, Simon
A1  - Tolvanen, Anne
A1  - Toniato, Maria Teresa Zugliani
A1  - Totland, Ørjan
A1  - Twilley, Robert R.
A1  - Vaitkus, Gediminas
A1  - Valdivia, Nelson
A1  - Vallejo, Martha Isabel
A1  - Valone, Thomas J.
A1  - Van Colen, Carl
A1  - Vanaverbeke, Jan
A1  - Venturoli, Fabio
A1  - Verheye, Hans M.
A1  - Vianna, Marcelo
A1  - Vieira, Rui P.
A1  - Vrška, Tomáš
A1  - Vu, Con Quang
A1  - Vu, Lien Van
A1  - Waide, Robert B.
A1  - Waldock, Conor
A1  - Watts, Dave
A1  - Webb, Sara
A1  - Wesołowski, Tomasz
A1  - White, Ethan P.
A1  - Widdicombe, Claire E.
A1  - Wilgers, Dustin
A1  - Williams, Richard
A1  - Williams, Stefan B.
A1  - Williamson, Mark
A1  - Willig, Michael R.
A1  - Willis, Trevor J.
A1  - Wipf, Sonja
A1  - Woods, Kerry D.
A1  - Woehler, Eric J.
A1  - Zawada, Kyle
A1  - Zettler, Michael L.
T1  - BioTIME: A database of biodiversity time series for the Anthropocene
JF  - Global Ecology and Biogeography
N2  - Motivation
The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.

Main types of variables included
The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.

Spatial location and grain
BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).

Time period and grain
BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.

Major taxa and level of measurement
BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.

Software format
.csv and .SQL.
KW  - biodiversity
KW  - global
KW  - spatial
KW  - species richness
KW  - temporal
KW  - turnover
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-222846
VL  - 27
ER  -