Community Monitoring of Environmental Change: College-Based Limnological Studies at Crazy Lake (Tasirluk), Nunavut

In light of the difficult logistics and high cost of polar research into climate change, involvement of local people can contribute immensely to important data collection. One can use the knowledge and skills of human resources that are already present—teachers, students, and community members. An example is the long-term Arctic monitoring program established at Crazy Lake (63°51' N, 68°28' W) near Iqaluit, Nunavut, to monitor snow and ice thickness, biological components, and water chemistry. Nunavut Arctic College students collected basic limnological data at Crazy Lake during spring field camps held between 10 and 16 April in 2005 and 2006. Mean snow depth ± SD for Crazy Lake was 0.46 ± 0.13 m (n = 24). White ice averaged 0.13 ± 0.12 m and black ice 1.38 ± 0.28 m. Total ice thickness (white ice + black ice) ranged between 0.91 and 1.91 m (mean = 1.51 ± 0.22 m). The total lake cover (snow + ice) averaged 1.97 ± 0.20 m. Water depth ranged from 1.48 to 18.58 m (mean = 10.10 ± 4.99 m).À la lumière de la complexité de la logistique et du coût élevé de la recherche polaire en matière de changement climatique, la participation des gens de la collectivité de la région à la collecte des données peut jouer un rôle très important en ce sens qu’il est possible de recourir aux connaissances et aux compétences des ressources humaines déjà en place, comme les enseignants, les élèves et les membres de la collectivité. Le programme de surveillance de l’Arctique de longue date établi au lac Crazy (63°51' N, 68°28' O) près d’Iqaluit, au Nunavut, en constitue un exemple. Ce programme vise à surveiller l’épaisseur de la neige et de la glace, de même que leurs composantes biologiques et la composition chimique de l’eau. Les élèves du collège Nunavut Arctic ont recueilli des données limnologiques de base au lac Crazy à l’occasion d’études sur le terrain réalisées au printemps 2005 et 2006, du 10 au 16 avril. Au lac Crazy, l’épaisseur moyenne de neige ± DS était de 0,46 ± 0,13 m (n = 24). La glace blanche atteignait en moyenne 0,13 ± 0,12 m et la glace noire, 1,38 ± 0,28 m. L’épaisseur totale de glace (glace blanche + glace noire) variait entre 0,91 et 1,91 m (moyenne = 1,51 ± 0,22 m). La couche du lac (neige + glace) atteignait en moyenne 1,97 ± 0,20 m, tandis que l’épaisseur de l’eau variait entre 1,48 et 18,58 m (moyenne = 10,10 ± 4,99 m)

Modulation of apoptosis sensitivity through the interplay with autophagic and proteasomal degradation pathways.

Autophagic and proteasomal degradation constitute the major cellular proteolysis pathways. Their physiological and pathophysiological adaptation and perturbation modulates the relative abundance of apoptosis-transducing proteins and thereby can positively or negatively adjust cell death susceptibility. In addition to balancing protein expression amounts, components of the autophagic and proteasomal degradation machineries directly interact with and co-regulate apoptosis signal transduction. The influence of autophagic and proteasomal activity on apoptosis susceptibility is now rapidly gaining more attention as a significant modulator of cell death signalling in the context of human health and disease. Here we present a concise and critical overview of the latest knowledge on the molecular interplay between apoptosis signalling, autophagy and proteasomal protein degradation. We highlight that these three pathways constitute an intricate signalling triangle that can govern and modulate cell fate decisions between death and survival. Owing to rapid research progress in recent years, it is now possible to provide detailed insight into the mechanisms of pathway crosstalk, common signalling nodes and the role of multi-functional proteins in co-regulating both protein degradation and cell death

Childrearing style of anxiety-disordered parents

FSW - Self-regulation models for health behavior and Psychopathology - Ou

Urinary tract infections in children after renal transplantation

Urinary tract infections (UTI) after pediatric kidney transplantation (KTX) are an important clinical problem and occur in 15–33% of patients. Febrile UTI, whether occurring in the transplanted kidney or the native kidney, should be differentiated from afebrile UTI. The latter may cause significant morbidity and is usually associated with acute graft dysfunction. Risk factors for (febrile) UTI include anatomical, functional, and demographic factors as well as baseline immunosuppression and foreign material, such as catheters and stents. Meticulous surveillance, diagnosis, and treatment of UTI is important to minimize acute morbidity and compromise of long-term graft function. In febrile UTI, parenteral antibiotics are usually indicated, although controlled data are not available. As most data concerning UTI have been accumulated retrospectively, future prospective studies have to be performed to clarify pathogenetic mechanisms and risk factors, improve prophylaxis and treatment, and ultimately optimize long-term renal graft survival

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

High contributions of sea ice derived carbon in polar bear (Ursus maritimus) tissue.

Polar bears (Ursus maritimus) rely upon Arctic sea ice as a physical habitat. Consequently, conservation assessments of polar bears identify the ongoing reduction in sea ice to represent a significant threat to their survival. However, the additional role of sea ice as a potential, indirect, source of energy to bears has been overlooked. Here we used the highly branched isoprenoid lipid biomarker-based index (H-Print) approach in combination with quantitative fatty acid signature analysis to show that sympagic (sea ice-associated), rather than pelagic, carbon contributions dominated the marine component of polar bear diet (72-100%; 99% CI, n = 55), irrespective of differences in diet composition. The lowest mean estimates of sympagic carbon were found in Baffin Bay bears, which were also exposed to the most rapidly increasing open water season. Therefore, our data illustrate that for future Arctic ecosystems that are likely to be characterised by reduced sea ice cover, polar bears will not only be impacted by a change in their physical habitat, but also potentially in the supply of energy to the ecosystems upon which they depend. This data represents the first quantifiable baseline that is critical for the assessment of likely ongoing changes in energy supply to Arctic predators as we move into an increasingly uncertain future for polar ecosystems

Community Monitoring of Environmental Change: College-Based Limnological Studies at Crazy Lake (Tasirluk), Nunavut

In light of the difficult logistics and high cost of polar research into climate change, involvement of local people can contribute immensely to important data collection. One can use the knowledge and skills of human resources that are already present—teachers, students, and community members. An example is the long-term Arctic monitoring program established at Crazy Lake (63°51' N, 68°28' W) near Iqaluit, Nunavut, to monitor snow and ice thickness, biological components, and water chemistry. Nunavut Arctic College students collected basic limnological data at Crazy Lake during spring field camps held between 10 and 16 April in 2005 and 2006. Mean snow depth ± SD for Crazy Lake was 0.46 ± 0.13 m (n = 24). White ice averaged 0.13 ± 0.12 m and black ice 1.38 ± 0.28 m. Total ice thickness (white ice + black ice) ranged between 0.91 and 1.91 m (mean = 1.51 ± 0.22 m). The total lake cover (snow + ice) averaged 1.97 ± 0.20 m. Water depth ranged from 1.48 to 18.58 m (mean = 10.10 ± 4.99 m).À la lumière de la complexité de la logistique et du coût élevé de la recherche polaire en matière de changement climatique, la participation des gens de la collectivité de la région à la collecte des données peut jouer un rôle très important en ce sens qu’il est possible de recourir aux connaissances et aux compétences des ressources humaines déjà en place, comme les enseignants, les élèves et les membres de la collectivité. Le programme de surveillance de l’Arctique de longue date établi au lac Crazy (63°51' N, 68°28' O) près d’Iqaluit, au Nunavut, en constitue un exemple. Ce programme vise à surveiller l’épaisseur de la neige et de la glace, de même que leurs composantes biologiques et la composition chimique de l’eau. Les élèves du collège Nunavut Arctic ont recueilli des données limnologiques de base au lac Crazy à l’occasion d’études sur le terrain réalisées au printemps 2005 et 2006, du 10 au 16 avril. Au lac Crazy, l’épaisseur moyenne de neige ± DS était de 0,46 ± 0,13 m (n = 24). La glace blanche atteignait en moyenne 0,13 ± 0,12 m et la glace noire, 1,38 ± 0,28 m. L’épaisseur totale de glace (glace blanche + glace noire) variait entre 0,91 et 1,91 m (moyenne = 1,51 ± 0,22 m). La couche du lac (neige + glace) atteignait en moyenne 1,97 ± 0,20 m, tandis que l’épaisseur de l’eau variait entre 1,48 et 18,58 m (moyenne = 10,10 ± 4,99 m)

Recovery From Reduction: The M’Clintock Channel Polar Bear Subpopulation, Nunavut, Canada

To retain viable polar bear subpopulations, scientific monitoring studies are conducted to inform adaptive management frameworks. Here we report the results of the second structured population study for polar bears in the M’Clintock Channel (MC) subpopulation. Data included biopsy samples collected during a 2014 – 16 subpopulation-wide survey, live mark-recapture data collected during the first subpopulation study from 1998 to 2000, and harvest recovery data from 1998 to 2016. Results of a closed capture-recapture model, implemented in a Bayesian framework for animals over 2 yr., produced a mean abundance estimate of 716 (95% Credible Interval = 545 – 955) for 2014 – 16, indicating an increase from the 1998 – 2000 study estimate (284; our Bayesian-calculated estimate: 325 bears). However, closed model assumptions mean our estimate represents the superpopulation. Mean litter sizes did not differ between study periods, but mean number of yearlings per adult female declined from 0.39 ± 0.10 (SE) to 0.27 ± 0.06 between 1998 – 2000 and 2014 – 16. Apparent survival estimates from observed data were biased low (0.88 ± 0.02) due to unknown immigration and emigration. However, survival calculated using the change in abundance estimates between study periods equaled 0.93, representing a population growth rate of 2%. Body condition improved between study periods. Our findings indicate the MC subpopulation recovered from overharvesting between 1979 and 1999 and may be transiently benefitting from increased biological productivity associated with local sea ice changes. Our demographic analyses were constrained by low density, low harvest, small sample sizes, low recapture probability, and lack of movement information; hence, harvest management decisions should be applied with appropriate caution. Des études de surveillance scientifique sont effectuées pour éclairer les cadres de gestion adaptative visant à garder des sous-populations d’ours polaires viables. Dans cet article, nous présentons les résultats de la deuxième étude structurée sur la population d’ours polaires composée de la sous-population du détroit de M’Clintock (MC). Les données comprenaient des échantillons de biopsies prélevés dans le cadre d’un relevé de l’ensemble de la sous-population réalisé de 2014 à 2016, des données réelles de marquage-recapture recueillies pendant la première étude de la sous-population de 1998 à 2000, et les données de récupération des récoltes de 1998 à 2016. Les résultats d’un modèle fermé de capture-recapture, appliqués dans un cadre bayésien pour les animaux de plus de deux ans, ont donné une estimation de l’abondance moyenne de 716 (intervalle de crédibilité de 95 % = 545 – 955) pour les années 2014 à 2016, ce qui représente une hausse par rapport à l’estimation de l’étude de 1998 à 2000 (284; notre estimation calculée selon le cadre bayésien : 325 ours). Cependant, les hypothèses du modèle fermé signifient que notre estimation représente la superpopulation. Les tailles de portées moyennes n’ont pas varié d’une période d’étude à l’autre, mais le nombre moyen d’ours d’un an par femelle adulte a diminué, passant de 0,39 ± 0,10 (ET) à 0,27 ± 0,06 de 1998 à 2000 et de 2014 à 2016. Les estimations du taux de survie apparente à partir des données observées ont fait l’objet d’un faible biais (0,88 ± 0,02) en raison du manque de données sur l’immigration et l’émigration. Toutefois, le calcul du taux de survie à l’aide de la variation des estimations de l’abondance entre les périodes étudiées correspondait à 0,93, soit un taux de croissance de la population de 2 %. Par ailleurs, la condition corporelle s’est améliorée entre les périodes étudiées. Selon nos constatations, la sous-population de MC s’est remise de la surchasse qui a eu cours de 1979 à 1999. Aussi, elle bénéficie peut-être, de façon transitoire, de la productivité biologique accrue découlant des changements touchant la glace de mer locale. Nos analyses démographiques ont été contraintes par la faible densité, le faible taux de récolte, la petite taille des échantillons, la faible probabilité de recapture et le manque d’information sur les déplacements. Par conséquent, les décisions en matière de gestion des récoltes devraient être prises avec circonspection.