Social Support at the Workplace, Motivation to Transfer, and Training Transfer: A Multi-Level Indirect Effects Model

Supervisor support, peer support and transfer motivation have been identified as important predictors for training transfer. Transfer motivation is supposed to mediate the support–training transfer relationship. Especially after team training interventions that include all team members (i.e., intact-team training), individual perception of these factors might be shared among team members. However, an integration of the team level in the training transfer process is rare, yet still needed. Analyzing 194 employees from 34 teams in the context of intact-team training interventions, we found similar relationships and processes at both levels of analysis: Social support enhances transfer motivation at the individual and team levels. Furthermore, motivation to transfer increases training transfer and serves as a connecting mechanism in the social support–training transfer link. The results underline the importance of (1) considering multiple levels in theories and research about the training transfer process and (2) ensuring the practice of individual-directed support and a shared, supportive climate within teams.

Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

Absence of somatostatin SST(2) receptor internalization in vivo after intravenous SOM230 application in the AR42J animal tumor model

Among clinically relevant somatostatin functions, agonist-induced somatostatin receptor subtype 2 (sst(2)) internalization is a potent mechanism for tumor targeting with sst(2) affine radioligands such as octreotide. Since, as opposed to octreotide, the second generation multi-somatostatin analog SOM230 (pasireotide) exhibits strong functional selectivity, it appeared of interest to evaluate its ability to affect sst(2) internalization in vivo. Rats bearing AR42J tumors endogenously expressing somatostatin sst(2) receptors were injected intravenously with SOM230 or with the [Tyr(3), Thr(8)]-octreotide (TATE) analog; they were euthanized at various time points; tumors and pancreas were analyzed by immunohistochemistry for the cellular localization of somatostatin sst(2) receptors. SOM230-induced sst(2) internalization was also evaluated in vitro by immunofluorescence microscopy in AR42J cells. At difference to the efficient in vivo sst(2) internalization triggered by intravenous [Tyr(3), Thr(8)]-octreotide, intravenous SOM230 did not elicit sst(2) internalization: immunohistochemically stained sst(2) in AR42J tumor cells and pancreatic cells were detectable at the cell surface at 2.5min, 10min, 1h, 6h, or 24h after SOM230 injection while sst(2) were found intracellularly after [Tyr(3), Thr(8)]-octreotide injection. The inability of stimulating sst(2) internalization by SOM230 was confirmed in vitro in AR42J cells by immunofluorescence microscopy. Furthermore, SOM230 was unable to antagonize agonist-induced sst(2) internalization, neither in vivo, nor in vitro. Therefore, SOM230 does not induce sst(2) internalization in vivo or in vitro in AR42J cells and pancreas, at difference to octreotide derivatives with comparable sst(2) binding affinities. These characteristics may point towards different tumor targeting but also to different desensitization properties of clinically applied SOM230.

The link between visual exploration and neuronal activity: A multi-modal study combining eye tracking, functional magnetic resonance imaging and transcranial magnetic stimulation

In the present multi-modal study we aimed to investigate the role of visual exploration in relation to the neuronal activity and performance during visuospatial processing. To this end, event related functional magnetic resonance imaging er-fMRI was combined with simultaneous eye tracking recording and transcranial magnetic stimulation (TMS). Two groups of twenty healthy subjects each performed an angle discrimination task with different levels of difficulty during er-fMRI. The number of fixations as a measure of visual exploration effort was chosen to predict blood oxygen level-dependent (BOLD) signal changes using the general linear model (GLM). Without TMS, a positive linear relationship between the visual exploration effort and the BOLD signal was found in a bilateral fronto-parietal cortical network, indicating that these regions reflect the increased number of fixations and the higher brain activity due to higher task demands. Furthermore, the relationship found between the number of fixations and the performance demonstrates the relevance of visual exploration for visuospatial task solving. In the TMS group, offline theta bursts TMS (TBS) was applied over the right posterior parietal cortex (PPC) before the fMRI experiment started. Compared to controls, TBS led to a reduced correlation between visual exploration and BOLD signal change in regions of the fronto-parietal network of the right hemisphere, indicating a disruption of the network. In contrast, an increased correlation was found in regions of the left hemisphere, suggesting an intent to compensate functionality of the disturbed areas. TBS led to fewer fixations and faster response time while keeping accuracy at the same level, indicating that subjects explored more than actually needed.

Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Effect of Population Heterogenization on the Reproducibility of Mouse Behavior: A Multi-Laboratory Study

In animal experiments, animals, husbandry and test procedures are traditionally standardized to maximize test sensitivity and minimize animal use, assuming that this will also guarantee reproducibility. However, by reducing within-experiment variation, standardization may limit inference to the specific experimental conditions. Indeed, we have recently shown in mice that standardization may generate spurious results in behavioral tests, accounting for poor reproducibility, and that this can be avoided by population heterogenization through systematic variation of experimental conditions. Here, we examined whether a simple form of heterogenization effectively improves reproducibility of test results in a multi-laboratory situation. Each of six laboratories independently ordered 64 female mice of two inbred strains (C57BL/6NCrl, DBA/2NCrl) and examined them for strain differences in five commonly used behavioral tests under two different experimental designs. In the standardized design, experimental conditions were standardized as much as possible in each laboratory, while they were systematically varied with respect to the animals' test age and cage enrichment in the heterogenized design. Although heterogenization tended to improve reproducibility by increasing within-experiment variation relative to between-experiment variation, the effect was too weak to account for the large variation between laboratories. However, our findings confirm the potential of systematic heterogenization for improving reproducibility of animal experiments and highlight the need for effective and practicable heterogenization strategies.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.