The edaphic macroarthropod communities of three sites representative of the forest succession in the Lake Duparquet region (situated in the southern part of the boreal forest, Abitibi, Quebec, Canada) were studied. A 47-year-old deciduous forest, a 144-year-old mixed forest, and a 231-year-old coniferous forest form a successional forest gradient with a vegetation composition that is a function of the time elapsed since the last forest fire. Along with forest succession, there is a reduction in macroarthropod community biodiversity. There is a negative gradient for mean total abundance (237-41 individuals), total richness (63-23 species), density (1792-661 individuals/m), diversity (H' = 1.478-1.007) and equitability (J = 0.821-0.739). These community changes affect saprophagous organisms more than predators. Certain species or taxa show preferences for a particular forest type, such as larval Diptera for the deciduous forest. A comparison of these results with the literature suggests an inverse preference as to habitat choice between microarthropods (Acarina and Collembola) and macroarthropods. From a conservation point of view, macroarthropod biodiversity will be favored by the protection of all forest types since each environment possesses a particular community structure and species.
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http://dx.doi.org/10.1007/s004420050289 | DOI Listing |
Nutr Rev
January 2025
Nutrition and Metabolism Research Group, Centre for Public Health, Queen's University Belfast Royal Victoria Hospital, Belfast BT12 6BJ, United Kingdom.
Context: Dietary protein is recommended for sarcopenia-a debilitating condition of age-related loss of muscle mass and strength that affects 27% of older adults. The effects of protein on muscle health may depend on protein quality.
Objective: The aim was to synthesize randomized controlled trial (RCT) data comparing plant with animal protein for muscle health.
Matern Child Health J
January 2025
Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia.
Introduction: Variable selection is a common technique to identify the most predictive variables from a pool of candidate predictors. Low prevalence predictors (LPPs) are frequently found in clinical data, yet few studies have explored their impact on model performance during variable selection. This study compared the Random Forest (RF) algorithm and stepwise regression (SWR) for variable selection using data from a paediatric sepsis screening tool, where 18 out of 32 predictors had a prevalence < 10%.
View Article and Find Full Text PDFJ Neurol
January 2025
Morehouse School of Medicine, Neuroscience Institute, 720 Westview Drive SW, Atlanta, GA, 30310, USA.
Objectives: The ability to differentiate epileptic- and non-epileptic events is challenging due to a lack of reliable molecular seizure biomarker that provide a retrospective diagnosis. Here, we use next generation sequencing methods on whole blood samples to identify changes in RNA expression following seizures.
Methods: Blood samples were obtained from 32 patients undergoing video electroencephalogram (vEEG) monitoring.
Naunyn Schmiedebergs Arch Pharmacol
January 2025
Department of Zoology, University of Allahabad, Senate House, University Road, Old Katra, Prayagraj, Uttar Pradesh, 211002, India.
This study was designed to evaluate the dose-dependent efficacy of neurotensin receptor-1 (NTSR1) agonist PD149163 in the amelioration of the lipopolysaccharide (LPS)-induced apoptosis in the gastrointestinal tract (GIT) of mice. PD149163 is an analogue of NTS, a GIT tri-decapeptide with anti-inflammatory and anti-oxidative effects. Swiss-albino mice (female/8 weeks/25 ± 2.
View Article and Find Full Text PDFJ Neuropsychiatry Clin Neurosci
January 2025
Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center and the Research and Academic Affairs Service Line, W. G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Department of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Department of Radiology (Hurley), Wake Forest University School of Medicine, Winston-Salem, N.C.
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