Results 71 to 80 of about 2,774 (200)

Glycogen storage disease type IV : A rare cause for neuromuscular disorders or often missed?

open access: yes, 2018
Advancements in genetic testing now allow early identification of previously unresolved neuromuscular phenotypes. To illustrate this, we here present diagnoses of glycogen storage disease IV (GSD IV) in two patients with hypotonia and delayed development
Dooijes, Dennis   +17 more
core   +1 more source

Mapping Scytonemin Pigment Content and Biocrust Functional Indicators Using High‐Resolution Multispectral Imagery

open access: yesRemote Sensing in Ecology and Conservation, EarlyView.
We demonstrate that high‐resolution multispectral imagery combined with machine learning can accurately predict key biocrust functional indicators, particularly the UV‐protective pigment scytonemin, across dryland ecosystems. Using fine‐scale imagery acquired in the Chihuahuan Desert and Colorado Plateau, we identified spectral bands strongly ...
Raúl Román   +8 more
wiley   +1 more source

Prenatal diagnosis of glycogen storage disease type IV

open access: yes, 2006
Background Glycogen storage disease type IV (GSD-IV) is a rare autosomal recessive disorder due to mutations in the GBE1 gene causing deficiency of the glycogen branching enzyme (GBE). Prenatal diagnosis has occasionally been performed by the measurement
Grigoriadou, Maria   +15 more
core  

Urinary tetraglucoside excretion as a biomarker in liver glycogen storage diseases [PDF]

open access: yes
INTRODUCTION: Increased urinary tetraglucoside (Glc4) excretions are associated with abnormal glycogen metabolism. While Glc4 is an established biomarker for glycogen storage disease (GSD) type II, a traditional muscle GSD, little data is available on ...
Gross-Valle, Candelas; id_orcid   +7 more
core   +1 more source

Fusion or Confusion? Assessing the Impact of Visible‐Thermal Image Fusion for Automated Wildlife Detection

open access: yesRemote Sensing in Ecology and Conservation, EarlyView.
This study evaluates the performances of synchronous aerial visible (VIS) and thermal infrared (TIR) imagery for detecting great blue heron (Ardea herodias) nests and individuals using a YOLO11n model. VIS and TIR images were automatically aligned using deep learning, and both early and late fusion approaches were tested.
Camille Dionne‐Pierre   +6 more
wiley   +1 more source

Queers Queering STEM: Reimagining Inclusive STEM Education

open access: yesJournal of Research in Science Teaching, EarlyView.
ABSTRACT Grounded in queer theory, this study explores the intersections of queerness and STEM trajectories through the lived experiences of three queer adults with postgraduate degrees in STEM and contributes their insights for queering STEM education.
Nelly K. M. Marosi   +2 more
wiley   +1 more source

Placental Involvement in Glycogen Storage Disease Type IV

open access: yes, 2008
Glycogen storage disease type IV (GSD IV) is a rare autosomal recessive disorder caused by glycogen branching enzyme (GBE) deficiency and resulting in the storage of abnormal glycogen (polyglucosan).
Dertinger, S.   +10 more
core  

Seasonal variations and challenges in estimating populations and identifying species of Korean ungulates using drone‐derived thermal orthomosaic maps

open access: yesWildlife Biology, EarlyView.
Drones equipped with thermal infrared (TIR) cameras offer significant time and labor savings in estimating wild ungulate populations. However, accurately monitoring forest‐dwelling ungulates remains challenging due to their elusive behavior and complex habitat.
Jinhwi Kim, Donggul Woo
wiley   +1 more source

Multispectral UAV Image Classification of Jimson Weed (Datura stramonium L.) in Common Bean (Phaseolus vulgaris L.)

open access: yesRemote Sensing
Jimson weed (Datura stramonium L.) is a toxic weed that is occasionally found in fields with common bean (Phaseolus vulgaris L.) for the processing industry. Common bean growers are required to manually remove toxic weeds.
Marlies Lauwers   +4 more
doaj   +1 more source

Evaluating machine learning models for multi‐species wildlife detection and identification on remote sensed nadir imagery in South African savanna

open access: yesWildlife Biology, EarlyView.
This research paper investigates the efficacy of leading machine learning (ML) models for detecting and identifying ungulate species in African savanna using nadir imagery from unmanned aerial vehicles (UAVs). Traditional aerial counting methods, while widely used, suffer from significant limitations in accuracy and precision, in part due to human ...
Paul Allin   +4 more
wiley   +1 more source

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