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.     

Self-Organized Monitoring in Ad-Hoc Networks

We propose in this paper a self-organized monitoring architecture for mobile ad-hoc networks based on a selective scheme where subsets of nodes are managed. These nodes are determined based on their network behavior in order to favor subsets of well-connected nodes. The key idea is to relax the requirements of the management plane, and to use these manageable subsets to monitor the performance of the overall network. We therefore propose a new performance metric to be monitored, in order to estimate the capability of ad-hoc nodes to communicate end-to-end in the network. Extensive simulations show how different parameters affect this metric.     

Multifractal analysis of non-conformal repellers: a model case

For a model class of repellers of non-conformal expanding maps, we obtain sharp lower bounds for the dimension spectra of the Birkhoff averages of a Hölder continuous function. As in the well-studied case of conformal dynamics, the bounds are also expressed here in terms of the thermodynamic formalism.     

The influence of some vegetable extracts on the in vitro adherence of mouse and human lymphocytes to nylon fibers

We found that the total watery extracts obtained from roots of various plants such as Symphytum officinale, Phytolacca americana etc, precipitate human glycoproteins, agglutinate sheep red blood cells (SRBC) and stimulate lymphocyte adherence to nylon fibers. Five out of seven extracts precipitated human gammaglobulins and one of seven obviously agglutinate SRBC. If these cells were pretreated with rabbit antibodies against SRBC, all extracts agglutinated the cells at various degrees of intensity, the most active being Phytolacca americana. The adherence of mouse but not human lymphocytes to nylon fibers was stimulated by extracts of Symphytum officinale and Phytolacca americana. This process was neither stimulated nor inhibited by Mannose (Man), Galactose (Gal), Glucose (Glc), N-acethyl Galactose (GalNAc) and N-acethyl Glucose (Glc-NAc). These biological effects of the plant extracts could be the expression of a lectin-like ability to bind various sugars other than those mentioned. The results suggest the possibility of using different extracts as means to point out the presence in serum or at the cellular level of some carbohydrates influencing the cellular adhesion, phenomenon which plays an important role in the functions of hematopoietic cells.     

Generating phenotypical erroneous human behavior to evaluate human–automation interaction using model checking

Breakdowns in complex systems often occur as a result of system elements interacting in unanticipated ways. In systems with human operators, human–automation interaction associated with both normative and erroneous human behavior can contribute to such failures. Model-driven design and analysis techniques provide engineers with formal methods tools and techniques capable of evaluating how human behavior can contribute to system failures. This paper presents a novel method for automatically generating task analytic models encompassing both normative and erroneous human behavior from normative task models. The generated erroneous behavior is capable of replicating Hollnagel's zero-order phenotypes of erroneous action for omissions, jumps, repetitions, and intrusions. Multiple phenotypical acts can occur in sequence, thus allowing for the generation of higher order phenotypes. The task behavior model pattern capable of generating erroneous behavior can be integrated into a formal system model so that system safety properties can be formally verified with a model checker. This allows analysts to prove that a human–automation interactive system (as represented by the model) will or will not satisfy safety properties with both normative and generated erroneous human behavior. We present benchmarks related to the size of the statespace and verification time of models to show how the erroneous human behavior generation process scales. We demonstrate the method with a case study: the operation of a radiation therapy machine. A potential problem resulting from a generated erroneous human action is discovered. A design intervention is presented which prevents this problem from occurring. We discuss how our method could be used to evaluate larger applications and recommend future paths of development.     

Well-Known and Novel Serum Biomarkers for Risk Stratification of Patients with Non-ischemic Dilated Cardiomyopathy

Non-ischemic dilated cardiomyopathy encompasses a wide spectrum of myocardial disorders, characterized by left ventricular dilatation with systolic impairment and increased risk of sudden cardiac death. In spite of all the therapeutic progress that has been made in recent years, dilated cardiomyopathy continues to be an important cause of cardiac transplant, being associated with an enormous cost burden for health care systems worldwide. Predicting the prognosis of patients with dilated cardiomyopathy is essential to individualize treatment. Late gadolinium enhancement-cardiac magnetic resonance imaging, microvolt T-wave alternans, and genetic testing have emerged as powerful tools in predicting sudden cardiac death occurrence and maximizing patient’s selection. Despite all these new diagnostic modalities, additional tests to complement or replace current tools are required for better risk stratification. Therefore, biomarkers are an easy and important tool that can help to detect patients at risk of adverse cardiovascular events. Additionally, identifying potential biomarkers involved in dilated cardiomyopathy can provide us important information regarding the diagnostic, prognostic, risk stratification, and response to treatment for these patients. Many potential biomarkers have been studied in patients with dilated cardiomyopathy, but only a few have been adopted in current practice. Therefore, the aim of our review is to provide the clinicians with an update on the well-known and novel biomarkers that can be useful for risk stratification of patients with non-ischemic dilated cardiomyopathy.     

Clustering based on median and closest string via rank distance with applications on DNA

This paper aims to present several clustering methods based on rank distance. Rank distance has applications in many different fields such as computational linguistics, biology and computer science. The K-means algorithm represents each cluster by a single mean vector. The mean vector is computed with respect to a distance measure. Two K-means algorithms based on rank distance are described in this paper. Hierarchical clustering builds models based on distance connectivity. This paper describes two hierarchical clustering techniques that use rank distance. Experiments using mitochondrial DNA sequences extracted from several mammals are performed to compare the results of the clustering methods. Results demonstrate the clustering performance and the utility of the proposed algorithms.     

K-means: Clustering by gradual data transformation

Traditional approach to clustering is to fit a model (partition or prototypes) for the given data. We propose a completely opposite approach by fitting the data into a given clustering model that is optimal for similar pathological data of equal size and dimensions. We then perform inverse transform from this pathological data back to the original data while refining the optimal clustering structure during the process. The key idea is that we do not need to find optimal global allocation of the prototypes. Instead, we only need to perform local fine-tuning of the clustering prototypes during the transformation in order to preserve the already optimal clustering structure.