Genetic predisposition for high stress reactivity amplifies effects of early-life adversity.

A dysregulation of the hypothalamus-pituitary-adrenocortical (HPA) axis and the experience of early-life adversity are both well-established risk factors for the development of affective disorders, such as major depression. However, little is known about the interaction of these two factors in shaping endophenotypes of the disease. Here, we studied the gene-environment interaction of a genetic predisposition for HPA axis dysregulation with early-life stress (ELS), assessing the short-, as well as the long-lasting consequences on emotional behavior, neuroendocrine functions and gene expression profiles. Three mouse lines, selectively bred for either high (HR), intermediate (IR), or low (LR) HPA axis reactivity, were exposed to one week of ELS using the limited nesting and bedding material paradigm. Measurements collected during or shortly after the ELS period showed that, regardless of genetic background, ELS exposure led to impaired weight gain and altered the animals' coping behavior under stressful conditions. However, only HR mice additionally showed significant changes in neuroendocrine stress responsiveness at a young age. Accordingly, adult HR mice also showed lasting consequences of ELS, including hyperactive stress-coping, HPA axis hyperreactivity, and gene expression changes in the Crh system, as well as downregulation of Fkbp5 in relevant brain regions. We suggest that the genetic predisposition for high stress reactivity interacts with ELS exposure by disturbing the suppression of corticosterone release during a critical period of brain development, thus exerting lasting programming effects on the HPA axis, presumably via epigenetic mechanisms. In concert, these changes lead to the emergence of important endophenotypes associated with affective disorders

Comparison of semiautomated bird song recognition with manual detection of recorded bird song samples

Automated recording units are increasingly being used to sample wildlife populations. These devices can produce large amounts of data that are difficult to process manually. However, the information in the recordings can be summarized with semiautomated sound recognition software. Our objective was to assess the utility of the semiautomated bird song recognizers to produce data useful for conservation and sustainable forest management applications. We compared detection data generated from expert-interpreted recordings of bird songs collected with automated recording units and data derived from a semiautomated recognition process. We recorded bird songs at 109 sites in boreal forest in 2013 and 2014 using automated recording units. We developed bird-song recognizers for 10 species using Song Scope software (Wildlife Acoustics) and each recognizer was used to scan a set of recordings that was also interpreted manually by an expert in birdsong identification. We used occupancy models to estimate the detection probability associated with each method. Based on these detection probability estimates we produced cumulative detection probability curves. In a second analysis we estimated detection probability of bird song recognizers using multiple 10-minute recordings for a single station and visit (35-63, 10-minute recordings in each of four one-week periods). Results show that the detection probability of most species from single 10-min recordings is substantially higher using expert-interpreted bird song recordings than using the song recognizer software. However, our results also indicate that detection probabilities for song recognizers can be significantly improved by using more than a single 10-minute recording, which can be easily done with little additional cost with the automate procedure. Based on these results we suggest that automated recording units and song recognizer software can be valuable tools to estimate detection probability and occupancy of boreal forest birds, when sampling for sufficiently long periods

Modelling vegetation understory cover using LiDAR metrics.

Forest understory vegetation is an important characteristic of the forest. Predicting and mapping understory is a critical need for forest management and conservation planning, but it has proved difficult with available methods to date. LiDAR has the potential to generate remotely sensed forest understory structure data, but this potential has yet to be fully validated. Our objective was to examine the capacity of LiDAR point cloud data to predict forest understory cover. We modeled ground-based observations of understory structure in three vertical strata (0.5 m to < 1.5 m, 1.5 m to < 2.5 m, 2.5 m to < 3.5 m) as a function of a variety of LiDAR metrics using both mixed-effects and Random Forest models. We compared four understory LiDAR metrics designed to control for the spatial heterogeneity of sampling density. The four metrics were highly correlated and they all produced high values of variance explained in mixed-effects models. The top-ranked model used a voxel-based understory metric along with vertical stratum (Akaike weight = 1, explained variance = 87%, cross-validation error = 15.6%). We found evidence of occlusion of LiDAR pulses in the lowest stratum but no evidence that the occlusion influenced the predictability of understory structure. The Random Forest model results were consistent with those of the mixed-effects models, in that all four understory LiDAR metrics were identified as important, along with vertical stratum. The Random Forest model explained 74.4% of the variance, but had a lower cross-validation error of 12.9%. We conclude that the best approach to predict understory structure is using the mixed-effects model with the voxel-based understory LiDAR metric along with vertical stratum, because it yielded the highest explained variance with the fewest number of variables. However, results show that other understory LiDAR metrics (fractional cover, normalized cover and leaf area density) would still be effective in mixed-effects and Random Forest modelling approaches

Explicit feedback to enhance the effect of an interim assessment: a cross-over study on learning effect and gender difference

Contains fulltext : 107832.pdf (publisher's version ) (Open Access)In a previous study we demonstrated by a prospective controlled design that an interim assessment during an ongoing small group work (SGW) session resulted in a higher score in the course examination. As this reflects the so-called testing effect, which is supposed to be enhanced by feedback, we investigated whether feedback following an interim assessment would have an effect on the score of the course exam, and whether the effect is influenced by the gender of the student. During a General Pathology bachelor course all 386 (bio) medical students took an interim assessment on the topics cell damage (first week) and tumour pathology (fourth week). The intervention consisted of immediate detailed oral feedback on the content of the questions of the interim assessment by the tutor, including the rationale of the correct and incorrect answers. It concerned a prospective randomized study using a cross-over design. Outcome measures were: (1) the difference in the normalized scores (1-10) of the course examination multiple choice questions related to the two topics, (2) effect of gender, and (3) gender-specific scores on formal examination. The effect of feedback was estimated as half the difference in the outcome between the two conditions. Mixed-model analysis was used whereby the SGW group was taken as the study target. The scores of the questions on cell damage amounted to 7.70 (SD 1.59) in the group without and 7.78 (SD 1.39) in the group with feedback, and 6.73 (SD 1.51) and 6.77 (SD 1.60), respectively, for those on tumour pathology. No statistically significant effect of feedback was found: 0.02 on a scale of 1-10 (95 % CI: -0.20; 0.25). There were no significant interactions of feedback with gender. Female students scored 0.43 points higher on the formal examination in comparison with their male colleagues. No additional effect of immediate explicit feedback following an interim assessment during an SGW session in an ongoing bachelor course could be demonstrated in this prospective randomized controlled study. Gender analysis revealed a higher performance of female students on the formal examination, which could not be explained by the effect of feedback in the current study. In this particular learning environment, SGW, explicit feedback may have little added value to the interactive learning that includes implicit feedback