Complementary Sensory and Associative Microcircuitry in Primary Olfactory Cortex

The three-layered primary olfactory (piriform) cortex is the largest component of the olfactory cortex. Sensory and intracortical inputs converge on principal cells in the anterior piriform cortex (aPC).Wecharacterize organization principles of the sensory and intracortical microcircuitry of layer II and III principal cells in acute slices of rat aPC using laser-scanning photostimulation and fast two-photon population Ca²⁺ imaging. Layer II and III principal cells are set up on a superficial-to-deep vertical axis. We found that the position on this axis correlates with input resistance and bursting behavior. These parameters scale with distinct patterns of incorporation into sensory and associative microcircuits, resulting in a converse gradient of sensory and intracortical inputs. In layer II, sensory circuits dominate superficial cells, whereas incorporation in intracortical circuits increases with depth. Layer III pyramidal cells receive more intracortical inputs than layer II pyramidal cells, but with an asymmetric dorsal offset. This microcircuit organization results in a diverse hybrid feedforward/recurrent network of neurons integrating varying ratios of intracortical and sensory input depending on a cell’s position on the superficial-to-deep vertical axis. Since burstiness of spiking correlates with both the cell’s location on this axis and its incorporation in intracortical microcircuitry, the neuronal output mode may encode a given cell’s involvement in sensory versus associative processing

How 'situational' is judgment in situational judgment tests?

Whereas situational judgment tests (SJTs) have traditionally been conceptualized as low-fidelity simulations with an emphasis on contextualized situation descriptions and context-dependent knowledge, a recent perspective views SJTs as measures of more general domain (context-independent) knowledge. In the current research, we contrasted these 2 perspectives in 3 studies by removing the situation descriptions (i.e., item stems) from SJTs. Across studies, the traditional contextualized SJT perspective was not supported for between 43% and 71% of the items because it did not make a significant difference whether the situation description was included or not for these items. These results were replicated across construct domains, samples, and response instructions. However, there was initial evidence that judgment in SJTs was more situational when (a) items measured job knowledge and skills and (b) response options denoted context-specific rules of action. Verbal protocol analyses confirmed that high scorers on SJTs without situation descriptions relied upon general rules about the effectiveness of the responses. Implications for SJT theory, research, and design are discussed

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%

Towards the next generation of 3D content creation

In this paper we present a novel integrated 3D editing environment that combines recent advantages in various fields of computer graphics, such as shape modelling, video-based Human Computer Interaction, force feedback and VR finemanipulation techniques. This integration allows us to create a new compelling form of 3D object creation and manipulation preserving the metaphors designers, artists and painters have accustomed to during their day to day practice. Our system comprises a novel augmented reality workbench and enables users to simultaneously perform natural fine pose determination of the edited object with one hand and model or paint the object with the other hand. The hardware setup features a non-intrusive, video-based hand tracking subsystem, see-through glasses and a 3D 6-degree of freedom input device. The possibilities delivered by our AR workbench enable us to implement traditional and recent editing metaphors in an immersive and fully threedimensional environment, as well as to develop novel approaches to 3D object interaction

Mesh Forging: Editing of 3D-Meshes Using Implicitly Defined Occluders

In recent years the ease of use and the flexibility in the editing process shifted into focus in modelling and animation applications. In this spirit we present a 3D mesh editing method that is similar to the simple constrained deformation (scodef) method 9. We extend this method to the so-called mesh forging paradigm by adding an occluder to the editing environment. Our method resembles and was in fact motivated by the forging process where an anvil is used to give the manipulated object the desired shape. While users perform the editing operation by directly manipulating the 3D-mesh, the occluder is defined implicitly. To enable fine detail edits even in sparsely triangulated areas, we propose an adaptive refinement method that also allows the creation of sharp features where desired. The functionality and ease of use of our editing approach is shown by several examples

Detecting holes in point set surfaces

Models of non-trivial objects resulting from a 3d data acquisition process (e.g. Laser Range Scanning) often contain holes due to occlusion, reflectance or transparency. As point set surfaces are unstructured surface representations with no adjacency or connectivity information, defining and detecting holes is a non-trivial task. In this paper we investigate properties of point sets to derive criteria for automatic hole detection. For each point, we combine several criteria into an integrated boundary probability. A final boundary loop extraction step uses this probability and exploits additional coherence properties of the boundary to derive a robust and automatic hole detection algorithm

Rapid Synchronous Acquisition of Geometry and Appearance of Cultural Heritage Artefacts

In order to produce visually appealing digital models of cultural heritage artefacts, a meticulous reconstruction of the 3D geometry alone is often not sufficient, as colour and reflectance information give essential clues of the object’s material. Standard texturing methods are often only able to overcome this fact under strict material and lighting condition limitations. The realistic reconstruction of complex yet frequently encountered materials such as fabric, leather, wood or metal is still a challenge. In this paper, we describe a novel system to acquire the 3Dgeometry of an object using its visual hull, recorded in multiple 2D images with a multi-camera array. At the same time, the material properties of the object are measured into Bidirectional Texture Functions (BTF), that faithfully capture the mesostructure of the surface and reconstruct the look-and-feel of its material. The high rendering fidelity of the acquired BTF texture data with respect to reflectance and self-shadowing also alleviates the limited precision of the visual hull approach for 3D geometry acquisition

Statistical shape analysis for computer aided spine deformity detection

In this paper we describe a medical application where we exploit surface properties (measured in form of 3D-Range scans of the human back) to derive a-priori unknown additional properties of the proband, that otherwise can only be acquired using multiple x-ray recordings or volumetric scans as CT or MRI. On the basis of 274 data sets, we perform classification using statistical shape analysis methods. Consistent parameterization and alignment is achieved on the basis of only few anatomic landmarks. As our choice of landmarks is easy to detect on the human body, our approach is feasible for screening applications that can be expected to have much impact on the early detection and later treatment of spine deformities, in particular scoliosis