FK506-binding protein 51 (encoded by
Fkpb51
, also known as
Fkbp5
) has been associated with stress-related mental illness. To investigate its function, we studied the morphological consequences of
...Fkbp51
deletion. Artificial Intelligence-assisted morphological analysis revealed that male
Fkbp51
knock-out (KO) mice possess more elongated dentate gyrus (DG) but shorter hippocampal height in coronal sections when compared to WT. Primary cultured
Fkbp51
KO hippocampal neurons were shown to exhibit larger dendritic outgrowth than wild-type (WT) controls and pharmacological manipulation experiments suggest that this may occur through the regulation of microtubule-associated protein. Both in vitro primary culture and in vivo labeling support a role for FKBP51 in the regulation of microtubule-associated protein expression. Furthermore,
Fkbp51
KO hippocampi exhibited decreases in βIII-tubulin, MAP2, and Tau protein levels, but a greater than 2.5-fold increase in Parkin protein. Overexpression and knock-down FKBP51 demonstrated that FKBP51 negatively regulates Parkin in a dose-dependent and ubiquitin-mediated manner. These results indicate a potential novel post-translational regulatory mechanism of Parkin by FKBP51 and the significance of their interaction on disease onset.
Graphical abstract
KO has more flattened hippocampus using AI-assisted measurement
Both pyramidal cell layer (PCL) of CA and granular cell layer (GCL) of DG distinguishable as two layers: deep cell layer and superficial layer. Distinct MAP2 expression between deep and superficial layer between KO and WT,
Higher Parkin expression in KO brain
Mechanism of FKBP51 inhibition resulting in Parkin, MAP2, Tau, and Tubulin expression differences between KO and WT mice, and resulting neurite outgrowth differences.
We study the problem of unknown foreground discovery in image streaming scenarios, where no prior information about the dynamic scene is assumed. Contrary to existing co-segmentation principles where ...the entire dataset is given, in streams new information emerges as content appears and disappears continually. Any object classes to be observed in the scene are unknown, therefore no detection model can be trained for the specific class set. We also assume there is no available repository of trained features from convolutional neural nets, i.e., transfer learning is not applicable. We focus on the progressive discovery of foreground, which may or may not correspond to contextual objects of interest, depending on the camera trajectory, or, in general, the perceived motion. Without any form of supervision, we construct in a bottom up fashion dynamic graphs that capture region saliency and relative topology. Such graphs are continually updated over time, and along with occlusion information, as fundamental property of the foreground–background relationship, foreground is computed for each frame of the stream. We validate our method using indoor and outdoor scenes of varying complexity with respect to content, objects motion, camera trajectory, and occlusions.
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•Bottom-up construction of the foreground, dynamically.•Fully unsupervised, without any prior knowledge or training.•Expands the existing notion of unsupervised detection to streaming data.•It is based on principles of co-segmentation while accommodating unseen data.•Light computation for integration with path planning/visual coverage in active exploration.
Respiratory rhythms arise from neurons situated in the ventral medulla. We are investigating their spatial and functional relationships optically by measuring changes in intracellular calcium using ...the fluorescent, calcium-sensitive dye Oregon Green 488 BAPTA-1 AM while simultaneously recording the regular firing of motoneurons in the phrenic nerve in isolated brainstem/spinal cord preparations of E17 to E19 mice. Responses of identified cells are associated breath by breath with inspiratory and expiratory phases of respiration and depend on CO2 and pH levels. Optical methods including two-photon microscopy are being developed together with computational analyses. Analysis of the spatial pattern of neuronal activity associated with respiratory rhythm, including cross-correlation analysis, reveals a network distributed in the ventral medulla with intermingling of neurons that are active during separate phases of the rhythm. Our experiments, aimed at testing whether initiation of the respiratory rhythm depends on pacemaker neurons, on networks or a combination of both, suggest an important role for networks.
We present a method for clustering data streams incrementally, designed to discover all valid density peaks in a single pass, in a non-parametric fashion. It detects emerging clusters along the ...stream by dynamically locating kernels in the most promising areas and performing a Stochastic Mean Shift procedure to find clustering centers. We present a density estimation approach for dynamic initialization, considering every sub-stream that follows 'emerging data' as a sample set and applying Hypothesis Testing (p-value approach) to estimate its local density. The sub-stream size and the p-value are determined in a way that provides provable accuracy guarantee. We compare our method with the state-of-the-art, on realistic and complex datasets. We show that it outperforms not only stream algorithms but also their more complex, non-stream foundational paradigms.
We present a fast and accurate center-based, single-pass clustering method, with main focus on improving the trade-off between accuracy and speed in computer vision problems, such as creating visual ...vocabularies. We use a stochastic mean-shift procedure to seek the local density peaks within a single pass of the data. We also present a dynamic kernel generation along with a density test procedure that finds the most promising kernel initializations. In our algorithm, we use two data structures, namely a dictionary of permanent kernels, and a short memory that is used to determine emerging kernels to be maintained and outliers to be discarded. We further develop a hierarchical realization of the algorithm such that it executes faster than most stream cluster algorithms, and the resulting tree serves as an efficient data structure to boost encoding procedure. We use our method for learning low-level image features on the fly, from unmanned aerial vehicle cameras, aiming at vision-driven maneuvering with reduced computational cost. In our experiments, we make extensive comparisons with popular clustering algorithms, with respect to accuracy and efficiency. Our algorithm showed improved accuracy and speed on datasets with sufficient cluster patterns. With noisy visual features, where natural clusters present inherent challenges (intra-cluster variability and inter-cluster similarities), we achieved high accuracy, compared to algorithms of higher complexity, while maintaining high efficiency: Only one method from the competition achieved lower run times, though with lower accuracy.
Drosophila Transmembrane channel-like (Tmc) is a protein that functions in larval proprioception. The closely related TMC1 protein is required for mammalian hearing and is a pore-forming subunit of ...the hair cell mechanotransduction channel. In hair cells, TMC1 is gated by small deflections of microvilli that produce tension on extracellular tip-links that connect adjacent villi. How Tmc might be gated in larval proprioceptors, which are neurons having a morphology that is completely distinct from hair cells, is unknown. Here, we have used high-speed confocal microscopy both to measure displacements of proprioceptive sensory dendrites during larval movement and to optically measure neural activity of the moving proprioceptors. Unexpectedly, the pattern of dendrite deformation for distinct neurons was unique and differed depending on the direction of locomotion: ddaE neuron dendrites were strongly curved by forward locomotion, while the dendrites of ddaD were more strongly deformed by backward locomotion. Furthermore, GCaMP6f calcium signals recorded in the proprioceptive neurons during locomotion indicated tuning to the direction of movement. ddaE showed strong activation during forward locomotion, while ddaD showed responses that were strongest during backward locomotion. Peripheral proprioceptive neurons in animals mutant for Tmc showed a near-complete loss of movement related calcium signals. As the strength of the responses of wild-type animals was correlated with dendrite curvature, we propose that Tmc channels may be activated by membrane curvature in dendrites that are exposed to strain. Our findings begin to explain how distinct cellular systems rely on a common molecular pathway for mechanosensory responses.
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•Three-dimensional morphologies of proprioceptive dendrites during movement are described•Drosophila larval proprioceptive neurons are tuned to direction of locomotion•ddaE responds to forward locomotion while ddaD responds to backward locomotion•Tmc channels are required for sensing movement direction
He et al. use high-speed confocal imaging to visualize the dynamic morphology of proprioceptive sensory neurons through the transparent skin of crawling Drosophila larvae. They use machine vision to create 3D models that describe how the neurons change during locomotion. They investigate the activity of the cells and the role of Tmc channels.
Current therapeutic angiogenesis strategies are focused on the development of biologically responsive scaffolds that can deliver multiple angiogenic cytokines and/or cells in ischemic regions. ...Herein, we report on a novel electrospinning approach to fabricate cytokine-containing nanofibrous scaffolds with tunable architecture to promote angiogenesis. Fiber diameter and uniformity were controlled by varying the concentration of the polymeric (i.e. gelatin) solution, the feed rate, needle to collector distance, and electric field potential between the collector plate and injection needle. Scaffold fiber orientation (random vs. aligned) was achieved by alternating the polarity of two parallel electrodes placed on the collector plate thus dictating fiber deposition patterns. Basic fibroblast growth factor (bFGF) was physically immobilized within the gelatin scaffolds at variable concentrations and human umbilical vein endothelial cells (HUVEC) were seeded on the top of the scaffolds. Cell proliferation and migration was assessed as a function of growth factor loading and scaffold architecture. HUVECs successfully adhered onto gelatin B scaffolds and cell proliferation was directly proportional to the loading concentrations of the growth factor (0–100bFGFng/mL). Fiber orientation had a pronounced effect on cell morphology and orientation. Cells were spread along the fibers of the electrospun scaffolds with the aligned orientation and developed a spindle-like morphology parallel to the scaffold’s fibers. In contrast, cells seeded onto the scaffolds with random fiber orientation, did not demonstrate any directionality and appeared to have a rounder shape. Capillary formation (i.e. sprouts length and number of sprouts per bead), assessed in a 3-D in vitro angiogenesis assay, was a function of bFGF loading concentration (0ng, 50ng and 100ng per scaffold) for both types of electrospun scaffolds (i.e. with aligned or random fiber orientation).
To develop and test a novel quantitative method of describing the corneoscleral junction, including metrics that reflect both the angle and the topography in this region of the ocular surface.
...Forty-eight neophyte subjects were recruited (16 Asian, 16 white, and 16 Latino). Optical coherence tomography images of the nasal, temporal, superior, and inferior quadrants in both eyes were taken. Custom image analysis software was written in Matlab to allow the observer to select a point defining the center of the junction, from which 20 concentric circles were automatically drawn. The surface of the junction in the image was automatically located by edge-detection routines, and the circles intersecting this edge defined a series of points in the Cartesian plane. A linear regression was fit to these points, and a set of metrics based on the regression residuals was calculated.
The sum of the squared orthogonalized residuals (SSRo) was the most repeatable metric and had the advantage of being unaffected by the orientation of the image. The SSRo was significantly greater in the nasal quadrant (p < 0.001), reflecting a more pronounced angle and/or rougher surface. The flattest and smoothest topography was found in the temporal quadrant. Whites had significantly higher SSRo than Asians and Latinos (p < 0.001).
This study presents a novel metric for characterizing the angle and topography of the corneoscleral junction using optical coherence tomography and establishes differences among quadrants and between ethnic groups.
Generative mixture models (MMs) provide one of the most popular methodologies for unsupervised data clustering. MMs are formulated on the basis of the assumption that each observation derives from ...(belongs to) a single cluster. However, in many applications, data may intuitively belong to multiple classes, thus rendering the single-cluster assignment assumptions of MMs irrelevant. Furthermore, even in applications where a single-cluster data assignment is required, the induced multinomial allocation of the modeled data points to the clusters derived by a MM, imposing the constraint that the membership probabilities of a data point across clusters sum to one, makes MMs very vulnerable to the presence of outliers in the clustered data sets, and renders them ineffective in discriminating between cases of equal evidence or ignorance. To resolve these issues, in this paper we introduce a possibilistic formulation of MMs. Possibilistic clustering is a methodology that yields possibilistic data partitions, with the obtained membership values being interpreted as degrees of possibility (compatibilities) of the data points with respect to the various clusters. We provide an efficient maximum-likelihood fitting algorithm for the proposed model, and we conduct an objective evaluation of its efficacy using benchmark data.
► A possibilistic clustering method based on mixture models is proposed. ► The method is illustrated for the case of Gaussian mixtures. ► An efficient EM algorithm is derived for model fitting.
We exploit the morphological stereotypy and relative simplicity of theDrosophilanervous system to model the diverse neuronal morphologies of individual motor neurons and understand underlying ...principles of synaptic connectivity in a motor circuit. In our analysis, we use images depicting single neurons labeled with green fluorescent protein (GFP) and serially imaged with laser scanning confocal microscopy. We model morphology with a novel formulation of Conditional Random Fields, ahierarchical latent-state CRF, to capture the highly varying compartment-based structure of the neurons (soma-axon-dendrites). In the training phase, we follow two approaches: (i) hierarchical learning, where compartment labels are given, and (ii) latent-state learning, where compartment labels are not given in the samples. We demonstrate the accuracy of our approach using wild-type motor neurons in the larval ventral nerve cord. However, our method can also be used for the identification of motor neuron mutations, as well as the automated annotation of the motor circuitry in wild type and mutant animals. Our method is directly applicable to the recognition of compartment-defined structures.
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