6-OHDA-induced dopaminergic neurodegeneration in <i>Caenorhabditis elegans</i> is promoted by the engulfment pathway and inhibited by the transthyretin-related protein TTR-33

<div><p>Oxidative stress is linked to many pathological conditions including the loss of dopaminergic neurons in Parkinson’s disease. The vast majority of disease cases appear to be caused by a combination of genetic mutations and environmental factors. We screened for genes protecting <i>Caenorhabditis elegans</i> dopaminergic neurons from oxidative stress induced by the neurotoxin 6-hydroxydopamine (6-OHDA) and identified the <u>t</u>rans<u>t</u>hyretin-<u>r</u>elated gene <i>ttr-33</i>. The only described <i>C</i>. <i>elegans</i> transthyretin-related protein to date, TTR-52, has been shown to mediate corpse engulfment as well as axon repair. We demonstrate that TTR-52 and TTR-33 have distinct roles. TTR-33 is likely produced in the posterior arcade cells in the head of <i>C</i>. <i>elegans</i> larvae and is predicted to be a secreted protein. TTR-33 protects <i>C</i>. <i>elegans</i> from oxidative stress induced by paraquat or H₂O₂ at an organismal level. The increased oxidative stress sensitivity of <i>ttr-33</i> mutants is alleviated by mutations affecting the KGB-1 MAPK kinase pathway, whereas it is enhanced by mutation of the JNK-1 MAPK kinase. Finally, we provide genetic evidence that the <i>C</i>. <i>elegans</i> cell corpse engulfment pathway is required for the degeneration of dopaminergic neurons after exposure to 6-OHDA. In summary, we describe a new neuroprotective mechanism and demonstrate that TTR-33 normally functions to protect dopaminergic neurons from oxidative stress-induced degeneration, potentially by acting as a secreted sensor or scavenger of oxidative stress.</p></div

Elderly with Autism: Executive Functions and Memory

Cognitive autism research is mainly focusing on children and young adults even though we know that autism is a life-long disorder and that healthy aging already has a strong impact on cognitive functioning. We compared the neuropsychological profile of 23 individuals with autism and 23 healthy controls (age range 51–83 years). Deficits were observed in attention, working memory, and fluency. Aging had a smaller impact on fluency in the high functioning autism (HFA) group than in the control group, while aging had a more profound effect on visual memory performance in the HFA group. Hence, we provide novel evidence that elderly with HFA have subtle neuropsychological deficits and that the developmental trajectories differ between elderly with and without HFA in particular cognitive domains

The cross-sectional GRAS sample: A comprehensive phenotypical data collection of schizophrenic patients

<p>Abstract</p> <p>Background</p> <p>Schizophrenia is the collective term for an exclusively clinically diagnosed, heterogeneous group of mental disorders with still obscure biological roots. Based on the assumption that valuable information about relevant genetic and environmental disease mechanisms can be obtained by association studies on patient cohorts of ≥ 1000 patients, if performed on detailed clinical datasets and quantifiable biological readouts, we generated a new schizophrenia data base, the GRAS (Göttingen Research Association for Schizophrenia) data collection. GRAS is the necessary ground to study genetic causes of the schizophrenic phenotype in a 'phenotype-based genetic association study' (PGAS). This approach is different from and complementary to the genome-wide association studies (GWAS) on schizophrenia.</p> <p>Methods</p> <p>For this purpose, 1085 patients were recruited between 2005 and 2010 by an invariable team of traveling investigators in a cross-sectional field study that comprised 23 German psychiatric hospitals. Additionally, chart records and discharge letters of all patients were collected.</p> <p>Results</p> <p>The corresponding dataset extracted and presented in form of an overview here, comprises biographic information, disease history, medication including side effects, and results of comprehensive cross-sectional psychopathological, neuropsychological, and neurological examinations. With >3000 data points per schizophrenic subject, this data base of living patients, who are also accessible for follow-up studies, provides a wide-ranging and standardized phenotype characterization of as yet unprecedented detail.</p> <p>Conclusions</p> <p>The GRAS data base will serve as prerequisite for PGAS, a novel approach to better understanding 'the schizophrenias' through exploring the contribution of genetic variation to the schizophrenic phenotypes.</p

Integrating spectral, spatial, and terrain variables for forest ecosystem classification

Sets of spectral, spectral-spatial, textural, and geomor- phometric variables derived fram high spatial resolution Compact Airborne Spectrographic Imager (CASI)and elevation data are tested to determine their ability to discriminate landscape-scale forest ecosystem classes for a study area in northern Ontario, Canada. First, linear discriminant analysis for various spectral and spectral-spatial variables indicated that a spatial resolution of approximately 6 m was optimal for discriminating six landscape-scaleforest ecosystem classes. Second, texture features, using second-order spatial statistics, significantly improved discrimination of the classes over the originalreflectance data. Finally, addition of terrain descriptors improved discrimination of the six forest ecosystem classes. It has been demonstrated that, in a low- to moderate-relief boreal environment,addition of textural and terrain variables to high- resolution CASI reflectance data provides improved discrim- ination of forest ecosystem classes

Differential Global Positioning System: potential for geographical information system database management

The primary problem for managers of digital topographic data is maintaining an accurate and up-to-date database. Traditional spatial-data-collection techniques and mapping procedures are expensive and, as a result, occur infrequently. However, the NAVSTAR Global Positioning System (GPS) now provides for the collection of timely, cost-effective spatial information. In this study, differential GPS data were collected for an area of rapid rural-to-urban land-use change by using low-cost GPS receivers in static and kinematic modes. These data were then processed for input to a geographic information system and assessed for their positional accuracy. It was found that GPS data collected in static mode and differentially corrected possessed a circular map accuracy standard (CMAS) of 3.62 m. These accuracies meet the requirements of many large-scale and medium-scale mapping programs.

A High Spatial Resolution Satellite Remote Sensing Time Series Analysis of Cape Bounty, Melville Island, Nunavut (2004–2018)

Changes in vegetation have been observed in areas of the Arctic due to changing climate. This study examines a normalized difference vegetation index (NDVI) time series (2004–2018) of high spatial resolution satellite data (i.e., IKONOS, WorldView-2, WorldView-3) to determine if vegetation abundance has changed over the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut. Image data were corrected to top-of-atmosphere reflectance and normalized for time series analysis using the pseudo-invariant feature (PIF) method. Percent vegetation cover measurements and indices derived from local climate data (growing degree days base 5 °C; GDD5) were used to contextualize NDVI trends in different vegetation types and within active layer detachments (ALDs). NDVI showed similar patterns within the different vegetation types and across the ALDs. There was no significant change in NDVI nor in GDD5 over time. However, there were statistically significant (p < 0.05) relationships between the GDD5 and NDVI for all vegetation types. Using field measurements with high spatial resolution remote sensing data helps link changes in NDVI with changes to vegetation and earth surface processes. The challenges of integrating high spatial resolution satellite data from different sensors in a time series analysis are also discussed

2006 Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density

Abstract: Light detection and ranging (lidar) is becoming an increasingly popular technology among scientists for the development of predictive models of forest biophysical variables. However, before this technology can be adopted with confidence for long-term monitoring applications in Canada, robust models must be developed that can be applied and validated over large and complex forested areas. This will require &quot;scaling-up&quot; from current models developed from high-density lidar data to low-density data collected at higher altitudes. This paper investigates the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables. Validation of results revealed that high-density models are well correlated with mean dominant height, basal area, crown closure, and average aboveground biomass (R 2 = 0.84, 0.89, 0.60, and 0.91, respectively). Low-density models could not accurately predict crown closure (R 2 = 0.36). However, they did provide slightly improved estimates for mean dominant height, basal area, and average aboveground biomass (R 2 = 0.90, 0.91, and 0.92, respectively). Maps were generated and validated for the entire study area from the low-density models. The ability of low-density models to accurately map key biophysical variables is a positive indicator for the utility of lidar data for monitoring large forested areas. Résumé : Le lidar est en voie de devenir une technique de plus en plus populaire parmi les chercheurs pour le déve-loppement de modèles de prédiction des variables biophysiques de la forêt. Cependant, avant que cette technologie puisse être adoptée avec confiance pour le suivi à long terme au Canada, des modèles robustes pouvant être appliqués et validés pour des superficies de forêt vastes et complexes doivent être développés. Cela va exiger de passer des modèles courants développés à partir d&apos;une forte densité de données lidar à une plus faible densité de données collectées à plus haute altitude. Cet article se penche sur l&apos;effet de la diminution de l&apos;espacement ponctuel moyen des échos individuels du lidar sur les modèles de variables biophysiques de la forêt. La validation des résultats a montré que les modèles à forte densité sont bien corrélés avec la hauteur dominante moyenne, la surface terrière, la fermeture du couvert et la biomasse aérienne moyenne (R 2 = 0,84, 0,89, 0,60 et 0,91 respectivement). Les modèles à faible densité ne pouvaient pas correctement (R 2 = 0,36) prédire la fermeture du couvert. Cependant, ils ont fourni des estimations légère-ment meilleures pour la hauteur dominante moyenne, la surface terrière et la biomasse aérienne moyenne (R 2 = 0,90, 0,91 et 0,92 respectivement). Des cartes ont été générées et validées pour toute la zone d&apos;étude à partir de modèles à faible densité. La capacité des modèles à faible densité à cartographier correctement les variables biophysiques importantes est une indication positive de l&apos;utilité des données lidar pour le suivi de vastes zones de forêt. [Traduit par la Rédaction] Thomas et al. 4