Impedimetric analysis of biological cell monolayers before and after exposure to nanosecond pulsed electric fields

Models and methods for the interpretation of impedance spectra for normal and cancer cells before and after electrical stimulation, focusing on nanosecond pulsed electric fields (nsPEFs), were investigated to describe salient features and their development that were observed in dedicated in situ experimental studies. For the first time a non-invasive, real-time and label-free method was established to explore temporal changes and their underlying physical processes of adherent cells for characteristics of cell-cell connections and the extracellular matrix.Modelle und Methoden zur Interpretation von Impedanzspektren für normale und Krebszellen vor und nach elektrischer Stimulation, mit dem Fokus auf Nanosekunden-gepulste elektrische Feldern, wurden untersucht, um herausragende Merkmale und deren Entwicklung zu beschreiben, die in speziellen In-situ-Experimenten beobachtet wurden. Zum ersten Mal wurde eine nicht-invasive, zeitnahe und markierungsfreie Methode entwickelt, um zeitliche Veränderungen und diesen zugrunde liegenden physikalischen Prozessen hinsichtlich der Eigenschaften von Zell-Zell-Verbindungen und der extrazellulären Matrix zu untersuchen

Predicting Short-term and Long-term HbA1c Response after Insulin Initiation in Patients with Type 2 Diabetes Mellitus using Machine Learning

AIM: To assess the potential of supervised machine learning techniques to identify clinical variables for predicting short-term and long-term glycated hemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: We included patients with T2DM from the Groningen Initiative to ANalyze Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007-2013 with a minimum follow-up of 2 years. Short-term and long-term response were defined at 6 (± 2) and 24 (± 2) months after insulin initiation, respectively. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty-four baseline clinical variables were used for the analysis and elastic net regularization technique was used for variables selection. The performance of three traditional machine learning algorithms was compared to predict short-term and long-term responses and the area under the receiver operator characteristic curve (AUC) was used to assess the performance of the prediction model. RESULTS: The elastic net regularization based generalized linear model, including baseline HbA1c and eGFR, correctly classified short-term and long-term HbA1c response after treatment initiation with an AUC (95% CI) = 0.80 (0.78 - 0.83) and 0.81 (0.79 - 0.84), respectively, and outperformed other machine learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (0.65 - 0.73) and 0.72 (0.66 - 0.75) was obtained for predicting short-term and long-term response, respectively. CONCLUSIONS: Machine-learning algorithm performed well in the prediction of an individual's short-term and long-term HbA1c response using baseline clinical variables. This article is protected by copyright. All rights reserved

The application of omics techniques to understand the role of the gut microbiota in inflammatory bowel disease

The aetiopathogenesis of inflammatory bowel diseases (IBD) involves the complex interaction between a patient’s genetic predisposition, environment, gut microbiota and immune system. Currently, however, it is not known if the distinctive perturbations of the gut microbiota that appear to accompany both Crohn’s disease and ulcerative colitis are the cause of, or the result of, the intestinal inflammation that characterizes IBD. With the utilization of novel systems biology technologies, we can now begin to understand not only details about compositional changes in the gut microbiota in IBD, but increasingly also the alterations in microbiota function that accompany these. Technologies such as metagenomics, metataxomics, metatranscriptomics, metaproteomics and metabonomics are therefore allowing us a deeper understanding of the role of the microbiota in IBD. Furthermore, the integration of these systems biology technologies through advancing computational and statistical techniques are beginning to understand the microbiome interactions that both contribute to health and diseased states in IBD. This review aims to explore how such systems biology technologies are advancing our understanding of the gut microbiota, and their potential role in delineating the aetiology, development and clinical care of IBD

Min–Max Hyperellipsoidal Clustering for Anomaly Detection in Network Security

A novel hyperellipsoidal clustering technique is presented for an intrusion-detection system in network security. Hyperellipsoidal clusters toward maximum intracluster similarity and minimum intercluster similarity are generated from training data sets. The novelty of the technique lies in the fact that the parameters needed to construct higher order data models in general multivariate Gaussian functions are incrementally derived from the data sets using accretive processes. The technique is implemented in a feedforward neural network that uses a Gaussian radial basis function as the model generator. An evaluation based on the inclusiveness and exclusiveness of samples with respect to specific criteria is applied to accretively learn the output clusters of the neural network. One significant advantage of this is its ability to detect individual anomaly types that are hard to detect with other anomaly-detection schemes. Applying this technique, several feature subsets of the tcptrace network-connection records that give above 95% detection at false-positive rates below 5% were identified

Fabrication, characterization of high-entropy alloys and deep learning-based inspection in metal additive manufacturing

Alloying has been used to confer desirable properties to materials. It typically involves the addition of small amounts of secondary elements to a primary element. In the past decade, however, a new alloying strategy that involves the combination of multiple principal elements in high concentrations to create new materials called high- entropy alloys (HEAs) has been in vogue. In the first part, the investigation focused on the fabrication process and property assessment of the additive manufactured HEA to broaden its engineering applications. Additive manufacturing (AM) is based on manufacturing philosophy through the layer-by-layer method and accomplish the near net-shaped components fabrication. Attempt was made to coat AlCoCrFeNi HEA on an AISI 304 stainless steel substrate to integrate their properties, however, it failed due to the cracks at the interface. The implementation of an intermediate layer improved the bond and eliminated the cracks. Next, an AlCoCrFeNiTi0.5 HEA coating was fabricated on the Ti6Al4V substrate, and its isothermal oxidation behavior was studied. The HEA coating effectively improved the Ti6Al4V substrate\u27s oxidation resistance due to the formation of continuous protective oxides. In the second part, research efforts were made on the deep learning-based quality inspection of additive manufactured products. The traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. A neural-network approach was developed toward robust real-world AM anomaly detection. The results indicate the promising application of the neural network in the AM industry --Abstract, page iv

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Forces and Flow of Contractile Networks

Biological cells use contractile networks of cross-linked semiflexible biopolymers, the so-called actin cytoskeleton, to control their shapes and to probe the mechanical properties of their environment. These processes are essential for cell survival and function. In this thesis we present a general framework to model two-dimensional contractile networks embedded in either two- or three-dimensional space. A surface representation with triangles and edges allows us to explicitly address the heterogeneity of biopolymer networks. In adherent cells, thick polymer bundles called stress fibers strongly influence cellular mechanics. We establish methods to assess their contribution to traction force generation, intracellular force balance, and intracellular flow from experimental data. Further, we develop a theory for the excitable nature of the cell cortex, which is a thin polymer layer lining the inner side of the cell membrane, and show how it is related to global cell shape changes