Gap Junctional Communication via Connexin43 between Purkinje Fibers and Working Myocytes Explains the Epicardial Activation Pattern in the Postnatal Mouse Left Ventricle

The mammalian ventricular myocardium forms a functional syncytium due to flow of electrical current mediated in part by gap junctions localized within intercalated disks. The connexin (Cx) subunit of gap junctions have direct and indirect roles in conduction of electrical impulse from the cardiac pacemaker via the cardiac conduction system (CCS) to working myocytes. Cx43 is the dominant isoform in these channels. We have studied the distribution of Cx43 junctions between the CCS and working myocytes in a transgenic mouse model, which had the His-Purkinje portion of the CCS labeled with green fluorescence protein. The highest number of such connections was found in a region about one-third of ventricular length above the apex, and it correlated with the peak proportion of Purkinje fibers (PFs) to the ventricular myocardium. At this location, on the septal surface of the left ventricle, the insulated left bundle branch split into the uninsulated network of PFs that continued to the free wall anteriorly and posteriorly. The second peak of PF abundance was present in the ventricular apex. Epicardial activation maps correspondingly placed the site of the first activation in the apical region, while some hearts presented more highly located breakthrough sites. Taken together, these results increase our understanding of the physiological pattern of ventricular activation and its morphological underpinning through detailed CCS anatomy and distribution of its gap junctional coupling to the working myocardium.     

Pessimistic cost-sensitive active learning of decision trees for profit maximizing targeting campaigns

In business applications such as direct marketing, decision-makers are required to choose the action which best maximizes a utility function. Cost-sensitive learning methods can help them achieve this goal. In this paper, we introduce Pessimistic Active Learning (PAL). PAL employs a novel pessimistic measure, which relies on confidence intervals and is used to balance the exploration/exploitation trade-off. In order to acquire an initial sample of labeled data, PAL applies orthogonal arrays of fractional factorial design. PAL was tested on ten datasets using a decision tree inducer. A comparison of these results to those of other methods indicates PAL’s superiority.     

Solvent-Induced Self-Assembly of Highly Hydrophobic Tetra- and Pentaphenylalanine Peptides

Diphenylalanine peptide (FF) self-assembles into ordered structures of notable physical properties. Moreover, the ability of the phenylalanine amino acid or triphenylalanine to assemble into ordered nanostructures had been demonstrated. Herein, we explored the association potential of larger phenylalanine peptides, tetraphenylalanine, and pentaphenylalanine. A major challenge in studying the assembly of these peptides is their lack of solubility in different solvents. Yet, the remarkable capacity of acetic acid to solubilize FF was recently shown. Inspired by this, we examined whether this solvent could also be employed to dissolve these insoluble peptides. By utilizing the solvent-switch methodology, we revealed the self-assembly of tetraphenylalanine and pentaphenylalanine. The peptides were assembled into ordered autofluorescent elongated structures, which were further characterized by electron microscopy and spectroscopy analysis and could be utilized in future technological applications.     

Viewpoint Selection for Human Actions

In many scenarios a dynamic scene is filmed by multiple video cameras located at different viewing positions. Visualizing such multi-view data on a single display raises an immediate question—which cameras capture better views of the scene? Typically, (e.g. in TV broadcasts) a human producer manually selects the best view. In this paper we wish to automate this process by evaluating the quality of a view, captured by every single camera. We regard human actions as three-dimensional shapes induced by their silhouettes in the space-time volume. The quality of a view is then evaluated based on features of the space-time shape, which correspond with limb visibility. Resting on these features, two view quality approaches are proposed. One is generic while the other can be trained to fit any preferred action recognition method. Our experiments show that the proposed view selection provide intuitive results which match common conventions. We further show that it improves action recognition results.     

Statistical analysis of dynamic actions

Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents.     

Hyaluronic Acid and a Short Peptide Improve the Performance of a PCL Electrospun Fibrous Scaffold Designed for Bone Tissue Engineering Applications

Bone tissue engineering is a rapidly developing, minimally invasive technique for regenerating lost bone with the aid of biomaterial scaffolds that mimic the structure and function of the extracellular matrix (ECM). Recently, scaffolds made of electrospun fibers have aroused interest due to their similarity to the ECM, and high porosity. Hyaluronic acid (HA) is an abundant component of the ECM and an attractive material for use in regenerative medicine; however, its processability by electrospinning is poor, and it must be used in combination with another polymer. Here, we used electrospinning to fabricate a composite scaffold with a core/shell morphology composed of polycaprolactone (PCL) polymer and HA and incorporating a short self-assembling peptide. The peptide includes the arginine-glycine-aspartic acid (RGD) motif and supports cellular attachment based on molecular recognition. Electron microscopy imaging demonstrated that the fibrous network of the scaffold resembles the ECM structure. In vitro biocompatibility assays revealed that MC3T3-E1 preosteoblasts adhered well to the scaffold and proliferated, with significant osteogenic differentiation and calcium mineralization. Our work emphasizes the potential of this multi-component approach by which electrospinning, molecular self-assembly, and molecular recognition motifs are combined, to generate a leading candidate to serve as a scaffold for bone tissue engineering.     

Bioinspired Flexible and Tough Layered Peptide Crystals

One major challenge of functional material fabrication is combining flexibility, strength, and toughness. In several biological and artificial systems, these desired mechanical properties are achieved by hierarchical architectures and various forms of anisotropy, as found in bones and nacre. Here, it is reported that crystals of N‐capped diphenylalanine, one of the most studied self‐assembling systems in nanotechnology, exhibit well‐ordered packing and diffraction of sub‐Å resolution, yet display an exceptionally flexible nature. To explore this flexibility, the mechanical properties of individual crystals are evaluated, assisted by density functional theory calculations. High‐resolution scanning electron microscopy reveals that the crystals are composed of layered self‐assembled structures. The observed combination of strength, toughness, and flexibility can therefore be explained in terms of weak interactions between rigid layers. These crystals represent a novel class of self‐assembled layered materials, which can be utilized for various technological applications, where a combination of usually contradictory mechanical properties is desired.