Sounds Stimulation on In Vitro HL1 Cells: A Pilot Study and a Theoretical Physical Model

Mechanical vibrations seem to affect the behaviour of different cell types and the functions of different organs. Pressure waves, including acoustic waves (sounds), could affect cytoskeletal molecules via coherent changes in their spatial organization and mechano-transduction signalling. We analyzed the sounds spectra and their fractal features. Cardiac muscle HL1 cells were exposed to different sounds, were stained for cytoskeletal markers (phalloidin, beta-actin, alpha-tubulin, alpha-actinin-1), and studied with multifractal analysis (using FracLac for ImageJ). A single cell was live-imaged and its dynamic contractility changes in response to each different sound were analysed (using Musclemotion for ImageJ). Different sound stimuli seem to influence the contractility and the spatial organization of HL1 cells, resulting in a different localization and fluorescence emission of cytoskeletal proteins. Since the cellular behaviour seems to correlate with the fractal structure of the sound used, we speculate that it can influence the cells by virtue of the different sound waves’ geometric properties that we have photographed and filmed. A theoretical physical model is proposed to explain our results, based on the coherent molecular dynamics. We stress the role of the systemic view in the understanding of the biological activity.     

Chain flexibility versus molecular entanglement response to rubbing deformation in designing poly(oxadiazole-naphthylimide)s as liquid crystal orientation layers

Four poly(oxadiazole-imide)s containing naphthalene rings, with different flexibility and molecular weight, are investigated with respect to their rheological properties to establish the optimal processing conditions from solution phase to film state for liquid crystal orientation purposes. The film uniformity and strength are determined by monitoring the flow behavior and chain entanglements. The solution rheological data are in agreement with film tensile testing, revealing that higher molecular weight favors chain entanglements and implicitly the film mechanical resistance. In order to analyze the suitability of these films as alignment layers their surface is patterned by rubbing with two types of velvet. Liquid crystal alignment of 4′-pentyl-4-biphenylcarbonitrile nematic is tested by polarized light microscopy. The resulting behavior is correlated with the polyimide malleability and characteristics of the textile fibers, namely their polarity, size, and mechanical features. The competitive effects between chain flexibility and entanglements, together with the interactions occurring between the polymer and velvet are analyzed in order to explain the surface regularity, which influences the uniformity of the liquid crystal alignment. The contrast between dark and bright states recorded on the liquid crystal cell indicates that some of these polynaphthalimides are promising candidates for liquid crystal display devices.     

Underapproximation of procedure summaries for integer programs

International Journal on Software Tools for Technology Transfer - We show how to underapproximate the procedure summaries of recursive programs over the integers using off-the-shelf analyzers for...     

Quantum Sensing in a Physiological‐Like Cell Niche Using Fluorescent Nanodiamonds Embedded in Electrospun Polymer Nanofibers

Fluorescent nanodiamonds (fNDs) containing nitrogen vacancy (NV) centers are promising candidates for quantum sensing in biological environments. This work describes the fabrication and implementation of electrospun poly lactic‐co‐glycolic acid (PLGA) nanofibers embedded with fNDs for optical quantum sensing in an environment, which recapitulates the nanoscale architecture and topography of the cell niche. A protocol that produces uniformly dispersed fNDs within electrospun nanofibers is demonstrated and the resulting fibers are characterized using fluorescent microscopy and scanning electron microscopy (SEM). Optically detected magnetic resonance (ODMR) and longitudinal spin relaxometry results for fNDs and embedded fNDs are compared. A new approach for fast detection of time varying magnetic fields external to the fND embedded nanofibers is demonstrated. ODMR spectra are successfully acquired from a culture of live differentiated neural stem cells functioning as a connected neural network grown on fND embedded nanofibers. This work advances the current state of the art in quantum sensing by providing a versatile sensing platform that can be tailored to produce physiological‐like cell niches to replicate biologically relevant growth environments and fast measurement protocols for the detection of co‐ordinated endogenous signals from clinically relevant populations of electrically active neuronal circuits.     

GOAL: A parsimonious geographic routing protocol for large scale sensor networks

Geographic routing is well suited for large scale sensor networks, because its per node state is independent of the network size. However, the local minimum caused by holes/obstacles results in the worst-case path stretch of Ω(c²), where c is the path length of the optimal route. Recently, a geographic routing protocol based on the visibility graph (VIGOR) showed that a path stretch of Θ(c) can be achieved. This path stretch, however, is achieved at the cost of communication and storage overhead, which makes the practical deployment of VIGOR in large scale sensor networks challenging. To this end, we propose GOAL (Geometric Routing using Abstracted Holes), a routing protocol that provably achieves a path stretch of Θ(c), with lower communication and storage overhead. To compactly describe holes, we develop a novel distributed convex hull algorithm, which improves the message complexity O(n log² n) of state of art distributed convex hull algorithm to O(n log n). The concise representation of a hole is used by nodes to make locally optimal routing decisions. Our theoretical analysis proves the correctness of the proposed algorithms and the path stretch of Θ(c). Through extensive simulations and experiments on a testbed with 42 EPIC motes, we demonstrate the effectiveness of GOAL and its feasibility for resource constrained wireless sensor networks; specifically, we show that GOAL eliminates part of communication overhead of VIGOR and reduces the memory overhead of VIGOR by up to 51%.     

Synthesis of probabilistic models for quality-of-service software engineering

An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability.     

Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.